Abstract Conventional models encounter challenges in detecting vehicle appearance components in intricate settings because of their limited small-target recognition capability and suboptimal fusion of multi-scale features. To address these issues, we propose an enhanced vehicle appearance segmentation model based on the YOLOv11-seg framework. Central to our approach is the MCALayerPlus module, designed to concurrently process targets across a wide range of scales. By executing multi-scale feature extraction, the model effectively suppresses false detections arising from cluttered backgrounds. Furthermore, we incorporate an improved ShapeIoU loss function, which integrates a size-sensitivity factor and a category-aware shape penalty term. This integration sharpens shape-matching precision, captures nuanced feature representations, and accelerates model convergence. Experimental results on a specialized automotive dataset demonstrate state-of-the-art performance, achieving a mean Average Precision (mAP@0.5) of 94.09%, an mAP@0.5:0.95 of 77.12%, precision of 91.31%, and recall of 90.75%. Notably, the model maintains a lightweight profile (5.75 MB), ensuring high-speed inference (45.3 FPS) suitable for real-time deployment in intelligent transportation systems. Similar content being viewed by others Data availability The datasets used and/or analyzed during the current study are available from the corresponding author. References Jocher, G., Chaurasia, A. & Qiu, J. Ultralytics YOLO [Computer software]. (2023). https://github.com/ultralytics/ultralytics Wang, C.-Y., Yeh, I.-H. & Liao, H.-Y. YOLOv9: Learning what you want to learn using programmable gradient information. arXiv https://doi.org/10.48550/arXiv.2402.13616 (2024). Wang, A. et al. YOLOv10: Real-Time End-to-End Object Detection. ArXiv, abs/2405.14458. (2024). Liu, Z. et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). : 10012–10022. (2021). Lv, W. et al. DETRs Beat YOLOs on Real-time Object Detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). : 16965–16974. (2024). Raj, V. et al. Smart traffic control for emergency vehicles prioritization using video and audio processing [C]//