An improved YOLOv11-based model with attention modules accurately detects unsafe miner behaviors in complex environments. It achieves high precision and enables real-time monitoring, enhancing safety and reducing accident risks in underground coal mining. A paper recently published in Scientific Reports proposed an effective and rapid method for detecting unsafe behaviors among underground coal mine personnel in complex coal mine environments. Coal Mining Safety Challenges Coal miners execute complex tasks in confined underground spaces, which require continuous vigilance to prevent unsafe behaviors and ensure miner safety. Yet, unique coal mine conditions and significant job pressures can occasionally lead to unsafe behaviors, resulting in serious injuries, accidents, or fatalities. Currently, personnel unsafe behavior identification in coal mines primarily relies on surveillance footage analysis and manual inspections, which pose challenges such as limited coverage of the operational area, high costs, low accuracy, significant subjectivity, and inefficiency. Thus, developing effective methods to prevent and detect unsafe behaviors among miners and improving coal mining operation safety standards are critical for the coal mining sector. Advances in Behavior Detection Constant advances in computer technology and machine vision have led to an increased application of machine vision in studies to detect unsafe behavior. Recent advances in unsafe behavior recognition approaches have relied on sensor fusion, deep learning (DL), machine learning (ML), and video analysis. Among ML techniques, random forest (RF) and support vector machine (SVM) are commonly employed in detecting unsafe behavior. However, high recognition accuracy cannot be realized using these methods in complex environments. DL methods, particularly Long Short-Term Memory Networks (LSTMs) and Convolutional Neural Networks (CNNs), have shown exceptional performance in time-series data and video analysis. For instance, unsafe behaviors by warehouse personnel were identified using CNN-based image classification methods that effectively handled issues such as changes in lighting conditions and occlusions. Recent research trends