The Office of the Australian Information Commissioner (OAIC) has published updates to its guidance for Australian Privacy Principle (APP) entities that are considering using facial recognition technology in high volume and publicly accessible physical spaces such as retail shopfronts. The updated guidance implements the findings of the Administrative Review Tribunal (ART) in the matter of Bunnings Group Limited (Bunnings), which concerned the retailer's use of facial recognition technology in 62 of its stores for a period between 2018 and 2021. In March 2026, the ART affirmed aspects of the Privacy Commissioner's November 2024 determination against Bunnings, and confirmed that there is a high bar for using facial recognition technology in Australia. These new guidance updates reflect the decision of the Tribunal and provide clarity about the OAIC's regulatory application of the law. The updated guidance provides greater clarity on how exceptions to the obligation to obtain consent when collecting sensitive information, including biometric information, should be applied in retail settings. As retailers will continue to need to make contextual assessments of the legality of using FRT on a case-by-case basis, the updated guidance seeks to support entities in undertaking those assessments. "The Bunnings decision by the ART provided important clarification on certain aspects of the Privacy Act, and this updated guidance incorporates those points of clarification. The guidance remains clear, however, that each proposed deployment of FRT will need to be assessed against the requirements of the Act," said the Privacy Commissioner. "A precautionary approach to the deployment of FRT is required under Australian law. This is consistent with the expectations of the Australian community, a significant and growing proportion of whom think facial recognition technology is one of the biggest privacy risks they face today (from 27% in 2023 to 45% 2026, according to the 2026 Australian Community Attitudes
Jul 29, 2026 · via miragenews.com
Flawed and racialised models that underpin the AI-powered age-detection systems to be introduced by the British government will endanger children, rights groups and children’s charities have warned. Urging ministers to reverse plans to introduce facial age-estimation technology to screen migrants, critics have warned that black children arriving from conflict zones are at risk of being of thrust into the adult system. Maddie Harris, of the Humans for Rights Network, said the charity had worked with hundreds of children who were already being wrongly filtered into the adult system during age assessments at borders carried out by immigration officers. In May, the Helen Bamber Foundation revealed that 755 children in 2025 were incorrectly identified as adults on arrival to the UK, according to Home Office figures. Harris said the introduction of AI was a “cheap process” that would not improve these numbers. “This is not about protecting children – this is about shoring up the decisions that [the authorities] are making,” she said. The government has admitted that even the best systems can have a 30-month margin of error. Harris said this is particularly risky for children, especially those from countries such as Sudan and Somalia, who she said were already “adultified” by the system. “As soon as a child is treated as an adult, they are susceptible to – or exposed, rather – to any of the egregious consequences that the Home Office is applying to adults, such as detention and removal,” she said. Unaccompanied children would be forced into accommodation alongside adults, exposing them to risk and to the violence threatened by the rise in protests targeting accommodation for asylum seekers, she said. In May, the government announced a contract to use facial age-estimation technology developed by Cognitec, a German firm that calls itself “the face-recognition company”. That system
Jul 29, 2026 · via theguardian.com
MP Moves Supreme Court Against Police Use Of Facial Recognition Technology & Biometric Surveillance At Protest Sites Gursimran Kaur Bakshi 28 July 2026 2:54 PM IST An Article 32 petition has been filed in the Supreme Court against the deployment of facial recognition technology(FRT) and allied biometric-surveillance measures by the Delhi police during the recent Jantar Mantar protest led by the Cockroach Janta Party. The petition has been filed by Rajya Sabha Member of Parliament, AA Rahim from Communist Party of India(M), Kerala, seeking a declaration that such indiscriminate biometric surveillance in peaceful assemblies is unconstitutional and should be restrained until the Parliament enacts a law validating it. The main grievance raised in the petition is that the Delhi police carried out surveillance in a complete legal vacuum. It has been argued that neither the Delhi Police standing orders governing protests, nor the Criminal Procedure(Identification) Act, 2022, authorises the biometric surveillance of persons in a civilian assembly. The petitioner has submitted that Delhi police carried out automated, algorithmic extraction and matching of the biometric identifiers of the protestors, and interlinking of such data with permanent national criminal databases. They have subjected the protestors, journalists and ordinary citizens to continuous and pervasive biometric surveillance through indiscriminate acquisition of video footage and photographs through CCTV cameras, drones, and a 'mobile command and control vehicle', as per the petitioner. The petitioner states that Delhi police's own RTI response confirms that they have never carried out a privacy impact assessment and that FRT is internally meant to be confined to tracing missing persons and identifying the dead. Relying on the Supreme Court's judgment on KS Puttaswamy v UOI(2017), which laid down the test of legality, legitimate aim and proportionality against the State's action, it says: "These objects are wholly unconnected with the policing of
Jul 28, 2026 · via livelaw.in
AI in Video Surveillance Technology Innovation: Key Trends, Growth Drivers and Opportunities Introduction to AI in Video Surveillance Technology Artificial Intelligence (AI) is transforming the video surveillance industry by enabling intelligent monitoring, real-time threat detection, and automated decision-making. Unlike traditional surveillance systems that rely heavily on manual monitoring, AI-powered video surveillance leverages computer vision, deep learning, machine learning, and edge AI to analyze video streams, recognize objects, detect anomalies, and generate actionable insights. These capabilities help organizations improve security, reduce response times, and optimize operational efficiency. As smart cities, intelligent transportation, critical infrastructure, retail, healthcare, and industrial facilities continue to adopt digital technologies, AI-powered video surveillance is becoming a key component of modern security and public safety strategies. History of AI in Video Surveillance Technology Video surveillance technology has evolved significantly from analog CCTV systems to intelligent, AI-driven surveillance platforms. Early surveillance systems primarily recorded footage for later review, requiring continuous human supervision to identify security incidents. The introduction of IP cameras, cloud computing, and advanced analytics marked the beginning of smarter surveillance solutions. Over the past decade, rapid advancements in artificial intelligence, computer vision, neural networks, and edge computing have enabled surveillance systems to automatically recognize faces, detect suspicious behavior, classify objects, and predict potential security threats. Today, AI-powered surveillance is widely used across commercial, industrial, transportation, and government sectors to enhance situational awareness and operational intelligence. Benefits of AI in Video Surveillance Technology AI-powered video surveillance delivers far more than traditional security monitoring by transforming video data into real-time intelligence. Automated analytics reduce the burden on security personnel while improving detection accuracy, minimizing false alarms, and accelerating incident response. These systems also generate valuable operational insights that help organizations optimize business processes, ensure regulatory compliance, and improve customer and employee safety. Key Benefits Include: - Enables real-time threat
Jul 28, 2026 · via marketsandmarkets.com
By Devin Fry FARGO, N.D. (Valley News Live) – Five surveillance cameras in Fargoâs Flock Safety network have the ability to lock onto and follow a person walking down the street using artificial intelligence, a capability that Flock Safety has publicly downplayed while marketing it in its own training materials. Fargo police confirmed the feature exists in the departmentâs five Pan-Tilt-Zoom, or PTZ, cameras, all of which are positioned along the Broadway corridor in downtown Fargo. The department says the tracking feature, called Guardian Mode, is turned off. âOur PTZ cameras do have the ability to identify through AI like vehicles and pedestrians,â said Capt. Matt Christensen, who oversees Fargoâs Real Time Crime Center. âIf you have the setting for pedestrians and a pedestrian starts walking by that camera, that camera will lock onto that pedestrian for a certain period of time.â Christensen said Fargo has chosen not to activate that feature. âWe have that turned off just because where theyâre at, that doesnât benefit us,â he said. What the cameras do Fargoâs Flock network includes 26 cameras total, 21 fixed Automated License Plate Readers, or ALPRs, and five PTZ cameras. The PTZ cameras replaced older downtown cameras when Fargo joined the Flock Safety network in June 2024, and are positioned in the same locations along Broadway that cameras have occupied for years. Unlike the fixed license plate readers, the PTZ cameras can pan, tilt and zoom. With Guardian Mode enabled, they use AI to detect movement and follow a subject, vehicle or person, within their field of view. Christensen said Fargoâs cameras are instead monitored and operated manually through the departmentâs Real Time Crime Center. âTheyâre monitored through the real-time crime center, so if we needed to move them for some reason, that would be done manually,â Christensen said.
Jul 28, 2026 · via wdayradionow.com
Hart County Sheriff’s Office discusses Flock camera policy and usage HART COUNTY, Ky. – The Hart County Sheriff’s Office is discussing its current policy regarding Automatic License Plate Readers, also known as Flock cameras. The agency stated in a social media post on Tuesday that it wanted to also discuss misconceptions about the cameras. According to the sheriff’s office, Flock cameras are “a public safety and investigative tool” utilized by its deputies. An example of use includes trying to catch an individual who steals a four-wheeler, the sheriff’s office states. The agency says the cameras are designed to capture an image of a license plate and a vehicle. According to the sheriff’s office, these images show the license plate number, vehicle make and model, color, time and location when a vehicle passed a camera. The agency says history on the cameras is available for up to 30 days. According to the sheriff’s office, after that time period passes, the data is purged, and inquiries are “always historic and not in real time.” The agency says a Flock system sends “alerts” for registration plates that are entered into the National Crime Information Center. Those alerts include wanted individuals, stolen vehicles, stolen registrations plates, golden alerts or amber alerts, missing individuals and an optional alert for sex offenders, according to the sheriff’s office. When an alert is dispatched, the agency says deputies in the area try to locate the vehicle. If the vehicle is found, the sheriff’s office says deputies are required to confirm the registration plate through dispatch or NCIC and follow proper traffic stop steps. What are some misconceptions, according to authorities? According to the sheriff’s office, Flock cameras do not possess facial recognition. In addition, the agency states after speaking with Flock representatives, Flock cameras do not offer any
Jul 28, 2026 · via wnky.com
FORM, the retail execution software company behind GoSpotCheck, today unveiled new AI-powered point-of-sale materials (POSM) image recognition capabilities that automatically detect, classify and extract in-store marketing compliance, campaign, and price data during store visits. The new capabilities give brands and retailers real-time visibility of in-store materials, displays, and promotions, so they can ensure their marketing investments are executed as planned. “Brands and retailers invest billions on trade spend and POSM production, yet most organizations lack a reliable way to confirm whether those materials are deployed in stores, correctly placed, or even present during a campaign's most critical moments,” said Alexander Zagvazdin, Chief Product Officer, FORM. “Our new POSM Recognition capability changes that, turning every field image into structured, actionable intelligence without adding friction to existing workflows.” POSM Recognition particularly benefits sectors where in-store display compliance has the highest business stakes, such as beer, wine and spirits (BWS), which typically have large-scale seasonal activations across thousands of accounts; quick service restaurants (QSR) with limited time offers; grocery retailers verifying private label and own-brand promotional execution; snacks and confectionery, as well as personal care brands with contractually defined display compliance obligations tied to retailer agreements. For leaders, the capability enables real-time campaign verification, faster identification of execution gaps, and POSM waste reduction by identifying materials that never reach the shelf. Also, it connects execution data to trade spend ROI, giving organizations the proof-of-performance they need to optimize future investment decisions. With POSM Recognition, data is delivered directly to customers’ existing GoSpotCheck workflow, in both PhotoWorks and the GoSpotCheck mobile app, ensuring that field reps, retail teams, supervisors and leaders all see the same standardized data without changing how they work. Built on next-generation AI that understands visual content in context, POSM Recognition delivers capabilities that other existing solutions cannot match: - Zero-shot,
Jul 28, 2026 · via brewbound.com
Designed to eliminate temporary storage requirements, and reduce delays for transport companies, Sunco Industries' new automated warehouse in Higashiosaka, Japan, represents a major investment in logistics automation, with the capacity to store approximately 13,000 pallets. Officially inaugurated during a completion ceremony on 1st October 2025, the facility combines advanced image recognition technology, automated dispatch systems, and dedicated storage for long products, helping the fastener distributor improve efficiency across its supply chain. During the completion ceremony, around 50 attendees, including Mr Okuyama, president at Sunco Industries, Mr Yoshikazu Noda, Mayor of Higashiosaka City, as well as Sunco employees and the construction team, gathered to mark the occasion. The event celebrated the successful completion of the warehouse and included a traditional prayer for its safety. President Okuyama delivered a speech explaining the reason behind relocating the previous warehouse and shared details on the features of this new investment: “This facility eliminates the need for temporary product storage space, allowing products to be automatically dispatched at the designated times for each delivery carrier. This reduces carrier waiting times and resolves issues caused by limited workspace.” The warehouse also includes a dedicated area for storing threaded rods, with capacity for items up to two metres in length. The most significant feature of the new warehouse is substantial improvement in collection efficiency for transport companies, thanks to the introduction of the rotary racks. These racks feature an image recognition system that enables fully automated banding – using cameras to detect whether cardboard cases lack plastic bands and applying them as needed. Previously, goods awaiting collection would remain on the warehouse floor, reducing operational efficiency. With the rotary rack, items are temporarily stored and automatically retrieved according to each carrier’s scheduled collection times, streamlining the handover process. This not only prevents overflows within the warehouse but
Jul 28, 2026 · via fastenerandfixing.com
Apple is about to kick off its major new smart home push. A new report from Bloomberg today reveals that Apple has three new smart home devices that are “nearly ready to launch.” The first two are set to come in just a couple of months … Apple’s three new smart home products coming soon According to the report, Apple is set to introduce a new Apple TV set-top box and an updated version of the HomePod mini as soon as this fall. These products “look like their current versions” but will feature faster chips inside to support Siri AI. For context, the current Apple TV 4K is powered by the A15 Bionic chip. The current HomePod mini features the S5 chip, which is the same processor as the Apple Watch Series 5. Perhaps more interestingly, Apple’s highly-anticipated new smart home hub device is also “nearly ready to launch.” This product is currently slated to debut “between October and early next year,” according to the report. There are two versions of this product in development. One of them “positions the screen on a half-dome-shaped base,” and the other is designed to be “affixed to a wall using a new magnetic system.” Today’s story also reiterates the specs and features of this device. It will reportedly have a 7-inch square display and is built around Siri AI. It features an “entirely new operating system” that’s built on tvOS. The interface is described as being a “blend” of tvOS and watchOS, with apps, icons, and widgets. It will also support FaceTime, home security monitoring, HomeKit integration, and customizable clock faces similar to the Apple Watch. The standout feature, though, is expected to be facial recognition: The hallmark feature is facial recognition, enabling the hub to identify who’s looking at the display, determine
Jul 28, 2026 · via 9to5mac.com
The allure of convenience AI-powered food image analysis offers a tantalizing vision of effortless nutrition tracking. Instead of manually logging every gram of food, users can simply photograph their meals and let the app do the work. This convenience is particularly attractive for athletes, who often need precise data to inform their fueling strategies. The idea of automating such a tedious process is understandably compelling. The market is already flooded with apps making bold claims. SnapCalorie advertises under 20% error rate, and Cal AI (now part of MyFitnessPal Photo) boasts 92-97% accuracy for common foods. And they aren’t alone, Nutrola, Cronometer, Foodvisor, and many others all claim similarly impressive numbers. But these often come with a critical caveat: they are typically collected on simple, single-food items like a banana or a chicken breast, not the multi-ingredient meals people actually eat. The hard truth A study by Fridolfsson et al. reveals a sobering reality. When AI attempts to estimate macronutrients from food images, the error rates range from 48% to 66%. The researchers also noted a systematic underestimation of large portions and high variability in macronutrient estimation. These errors are not minor. They are significant enough to render the data unreliable for serious applications. A 2026 systematic review in PMC further confirms this pattern. While AI performs well on simple foods, its accuracy drops sharply for multi-ingredient dishes. For example, a stir-fry or a casserole, where ingredients are visually intermingled, poses significant challenges. The AI might identify “chicken” and “rice,” but it cannot reliably determine the weight of either. Why it’s so difficult The challenges of AI-based food analysis are numerous. Visually identical foods, such as white rice and cauliflower rice, can be nearly impossible for AI to distinguish. Hidden ingredients like oils, sauces, and seasonings further complicate accurate estimation. For
Jul 28, 2026 · via welovecycling.com
Kiwis could soon be able to pay for a coffee or a pair of jeans using their face or hand. Eftpos New Zealand is launching new biometric-capable payment terminals developed by manufacturer Verifone. The Victa terminals include biometric capabilities around facial recognition and palm vein technology. New Zealand will beone of the first markets globally to have access to the innovation this month. The technology will enable age verification, digital identity and secure payments to happen in a single interaction at the terminal, without the need for a physical card. Verifone believes applications for the new technology are broad and make sense wherever age or identity verification is required, such as at liquor stores or hospitality venues. The company has already completed proof-of-concept trials internationally and the technology is currently being trialled at coffee chain Starbucks overseas. Eftpos New Zealand head of merchant sales and strategic partners Kristy Gregory said the company works with the biggest retailers in New Zealand, so Kiwis can expect them to add a biometrics offering to their businesses in the near future. “As the technology is rolled out by retailers, New Zealanders will have different ways they can pay, including biometrics for those who choose it,” Gregory said. “Eftpos has been part of daily life here for over 40 years. We have the infrastructure, the relationships and the reach to ensure New Zealanders have access to the most capable, most secure payments platform in the world.” Gregory said the evolution of the technology was driven by a shift in what the purpose of a payment terminal is. She said that rather than operating as a single-purpose device that processes transactions, the goal was a platform that runs loyalty programmes, ordering, digital identity, analytics and third-party applications from a single device at the counter. The terminals
Jul 28, 2026 · via nzherald.co.nz
Quick Navigation - Report Overview - Top Market Takeaways - By Component Analysis - By Deployment Analysis - By Enterprise Size Analysis - Key Market Segments - Regional Analysis - Investor Type Impact Matrix - Technology Enablement Analysis - Key Challenges - Emerging Trends - Growth Factors - Drivers Impact Analysis - Restraints Impact Analysis - Competitive Analysis - Future Outlook - Recent Developments - Report Scope Report Overview The Global Remote Deposit Capture Market generated USD 840.5 million in 2025 and is predicted to register growth from USD 910.3 million in 2026 to about USD 1,865.6 million by 2035, recording a CAGR of 8.3% throughout the forecast span. In 2025, North America held a dominant market position, capturing more than a 39.2% share, holding USD 329.47 million in revenue. Remote Deposit Capture market refers to banking technology that allows users to deposit checks remotely by scanning or photographing them and sending the image to the bank through a mobile app, desktop scanner, or online banking platform. It is widely used by retail banking customers, small businesses, and enterprises that still handle check-based payments as part of their regular transactions. A key factor driving this market is the growing preference for digital banking services that save time and reduce manual effort. Customers increasingly expect banking activities to be completed from any location, while financial institutions want to improve service efficiency without expanding branch operations. The continued use of checks in business payments also supports market growth. Top Market Takeaways - By Component, solutions dominate with a 76.5% share, delivering mobile capture apps, backend scanning platforms, and AI-powered validation for seamless deposit workflows. - By Deployment, on-premises captures 62.8%, ensuring data sovereignty, integration with core banking systems, and compliance with strict regulatory retention policies. - By Enterprises, large enterprises hold 65.9%,
Jul 28, 2026 · via market.us
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Jul 28, 2026 · via youtube.com
Essex Police resume Corsight AI LFR with 57 arrests, no false matches Resumed deployments of live facial recognition from Corsight AI by Essex Police have led to dozens of arrests without a single false positive match across scans of 353,000 faces, according to a company announcement. Essex Police began redeploying the biometric technology in April, after they were paused for an ICO audit last August. In the meantime, an evaluation by the UK National Physical Laboratory showed a True Positive Identification Rate (TPIR) of 89 percent with a False Positive Identification Rate (FPIR) of 0.017 percent, or 1 in 5,700 matches, with a watchlist of 18,000 images. Cambridge University performed a series of evaluations with the live facial recognition software’s matching threshold set at 55, and found just over half of those on the watchlist were correctly identified, while false matches were extremely rare. Corsight supplies the facial recognition algorithms, and Digital Barriers supplies the software and cloud solutions they run on. Essex Police have now carried out 22 deployments since resuming the program, and of the 353,000 attempted matches, received 173 alerts, 143 of which resulted in interventions. Police report 57 arrests stemming the LFR use, but zero false positive matches. The College of Policing’s guidance on live facial recognition suggests an FPIR of around 1 in 1,000 is acceptable. Essex Police would not have been able to make the same 143 interventions using the former ad hoc approach instead of LFR, Corsight says based on departmental data. Corsight AI President Rob Watts says the results show the effectiveness of “Facial Intelligence” that “is built for real-world conditions” for making police actions more effective and more proportionate at the same time. “Deploying Live Facial Recognition isn’t just about introducing new technology, it’s about ensuring it’s implemented responsibly, transparently, and
Jul 28, 2026 · via biometricupdate.com
Recent reports have raised alarm about the use of PatronScan, an ID-checking and face-scanning system, at multiple LGBTQ+ bars in San Francisco’s Castro neighborhood. Much of the attention has focused on reports that the system photographs patrons as they enter venues and questions about whether those images are used for facial recognition. A broader privacy concern also deserves scrutiny. For years, PatronScan has marketed itself not just as an ID-verification tool, but as a system that allows bars and clubs to identify patrons, keep records about them, and share information across venues. As one news article published in 2019 documented, PatronScan built a network that allowed participating bars to flag patrons and share information about them with other establishments. And in California, it’s not at all clear how PatronScan’s business model of scanning IDs and sharing the information from those scans with other bars comports with the law. California’s ID privacy law, which was amended in 2018 to add ID “scans,” states that no businesses shall “retain or use” any information from a scanned ID card except for limited purposes such as to verify age, comply with a legal requirement, or prevent fraud. A venue cannot claim to be a safe space while feeding its patrons’ data to a third party database. Californians should be deeply concerned about businesses that collect information from government-issued IDs and use it to build databases about where people go, whom they associate with, and whether they should be allowed into other public gathering places. That concern is especially strong in LGBTQ+ spaces, which have long served as refuges for people to go without being tracked, monitored, or put on lists. We reached out to Patronscan with questions regarding their practices and their views on California ID law. They referred us to their published FAQ
Jul 28, 2026 · via eff.org
Facial recognition vans to be deployed by police - Published Facial recognition cameras being rolled out in Nottinghamshire will come with strict controls, police have said. Marked vans will be deployed in public places and will compare those walking past with a "watch list" of individuals either wanted for crimes or subject to banning orders or reported missing. Any matches made by the system will be verified by officers on the ground and all scans deleted within seconds, the force said. Nottinghamshire Police insisted the technology would be used "fairly and proportionately" and would not mean fewer officers being available. The technology has been used by other forces, Nottinghamshire Police said, and had proved a valuable tool. Det Supt Will Henley, who is leading the project, said: "This new technology is not interested in tracking your movements. "It is designed to identify those faces we have inputted into the system who are wanted for crimes or in need of safeguarding such as locating missing people. "The technology will always be used fairly and proportionately to the area it is deployed. "This will not replace police officers." Henley acknowledged the cameras might raise concerns, but emphasised no information from the cameras was stored and the software did not discriminate on the grounds of gender, age or race. The marked van also works as a deterrent and will hopefully prevent further criminality from occurring when they are deployed across our neighbourhoods, he said. The results of deployments will also be posted on the Nottinghamshire Police website. Get in touch Tell us which stories we should cover in Nottingham Listen to BBC Radio Nottingham on Sounds and follow BBC Nottingham on Facebook, external, on X, external, or on Instagram, external. Send your story ideas to eastmidsnews@bbc.co.uk, external or via WhatsApp, external on 0808
Jul 28, 2026 · via bbc.co.uk
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Jul 28, 2026 · via youtube.com
Abstract With the rapid development of intelligence technology, inspection robots for high-speed Electric Multiple Unit (EMU) maintenance have emerged as a promising solution. However, a mature defect detection algorithm and framework tailored specifically for high-speed EMU inspection robots remains lacking. To address this gap, this paper proposes a position-prior-based multimodal defect detection framework capable of accurately identifying two primary types of component defects: component missing and bolt looseness. The proposed framework in this paper is designed with a two-stage methodology, which includes a component detection stage and a defect detection stage for identifying missing components and bolt looseness. To address the component missing defect, the paper initially established defect-free standard images and corresponding component positions for the same vehicle model. Following this, a Class-Similarity Iterative Closest Point (CS-ICP) approach is introduced. The proposed method represents the component positions from both the defect-free reference and the inspected images as semantic points enriched with category information, and diagnoses component absence by performing point cloud registration on these semantic point sets. To address the bolt looseness defect, the point cloud in the bolt area is first preprocessed via filtering. Then, a RANSAC algorithm incorporating normal vector constraints, as proposed in this study, is employed to precisely segment the top and bottom planes of the bolt. Based on this, the bolt height is computed to identify potential looseness defects. The proposed method has been experimentally validated on both the high-speed EMU inspection robot platform deployed in a high-speed railway maintenance depot and the Train of EMU failures Detection System(TEDS). Experimental results demonstrate that the method accurately diagnoses the primary types of component defects, exhibiting high accuracy and strong practical applicability. References - Lourenço A, Ribeiro D, Fernandes M, Marreiros G (2024) Time series data mining for railway wheel and track monitoring: a survey. Neural
Jul 28, 2026 · via link.springer.com
Machine Vision Camera Market Size, Share, Growth, and Industry Analysis, By Type (Line Scan, Area Scan, 3D), By Application (Medical, Industrial, Other), Regional Insights and Forecast to 2035 Machine Vision Camera Market Overview The global Machine Vision Camera Market is likely to grow from USD 13103.54 million in 2026 to USD 20602.46 million in 2035, with an average CAGR of 5.82% during the forecast period. The United States machine vision camera industry is experiencing steady expansion due to increasing automation across manufacturing, healthcare, and quality inspection applications. The adoption of advanced imaging systems has accelerated as industries focus on improving productivity, with more than 70% of modern production facilities integrating automated inspection technologies. Growth in artificial intelligence-based image processing, high-resolution imaging, and smart factory initiatives is supporting demand for Line Scan, Area Scan, and 3D camera solutions across industrial environments. Medical imaging applications are also expanding as healthcare providers increasingly adopt precision imaging technologies for diagnostics and research activities. Key Findings - Leading Product Type: Area Scan cameras are expected to maintain the largest adoption share due to broad industrial usage, with approximately 60% preference among machine vision installations requiring detailed surface inspection. - Leading Application: Industrial applications are projected to dominate demand as more than 65% of automated inspection systems rely on machine vision cameras for manufacturing quality control and process optimization. - Leading Region: North America is expected to lead the market due to advanced automation adoption, with over 50% of large manufacturers implementing smart inspection technologies across production facilities. - Fastest Growing Region: Asia Pacific is projected to witness rapid expansion with manufacturing automation investments increasing by more than 15% annually across electronics and automotive production sectors. - Technology Trend: Artificial intelligence-powered vision systems are transforming camera capabilities, with AI-based inspection accuracy improving by nearly 30% compared
Jul 28, 2026 · via marketgrowthreports.com
BEIJING – Lai Ka-ying, the first astronaut from the Hong Kong Special Administrative Region, and her two crewmates on the Shenzhou XXIII space mission have recently completed a study on gut microbiome changes as a result of long-duration spaceflight, as they reached their 60th day in orbit aboard China's space station. During this period, Lai and her colleagues Zhu Yangzhu and Zhang Zhiyuan have also systematically carried out a range of tasks, including multiple scientific experiments, maintenance of the space station complex platform, and in-orbit health management, according to the China Manned Space Agency (CMSA). Using a space Raman spectrometer, the crew conducted research on microbiome and nutritional metabolism to analyze changes in intestinal flora and their effects on nutrient metabolism under extended spaceflight conditions. They also performed a force control test to investigate the patterns of fine motor control changes and adaptive learning mechanisms during long-term orbital missions. ALSO READ: HK’s first astronaut, crewmates conduct space physiology experiments In-orbit rendezvous and docking training was recently carried out as scheduled, during which the crew completed image recognition exercises under various initial conditions using a metacognitive training system. The CMSA has recently opened a call for proposals for the space station's 2026 scientific research and application program, focusing on three major areas: space life sciences and biotechnology, space microgravity physics, and new space technologies and applications. READ MORE: Lai, crewmates complete installation of HK-made ‘eye in space’ The program covers 23 specific research directions, including frontier exploration in space life sciences, mechanistic studies on material preparation processes under microgravity, and in-orbit manufacturing and construction technologies.
Jul 28, 2026 · via chinadailyasia.com