Application of semi-supervised object detection technology incorporating attention mechanism in rail transit - WANG Junyu; CHEN Zhe; SUN Junyong (机车电传动)
The operation and maintenance of rail transit systems require high-accuracy object detection and tracking technology to ensure the safety of passengers and the normal operation of traffic systems. In recent years, with the emergence of Vision in Transformer (VIT), large models incorporating attention mechanisms have garnered widespread application in the field of object detection. However, attention mechanisms require substantial demands of data in the process of model training to achieve high precision and robustness. Moreover, the process of image data processing is often accompanied by significant consumption of manpower and material resources. This paper proposes a semi-supervised object detection strategy that incorporates an attention mechanism, to reduce the cost of data processing and improve the robustness of the models. Detectors such as Grounding DINO and YOLO-World are employed as the backbone of the algorithm, and attention mechanisms such as CBAM, CoTAttention, and SEAttention are applied at the algorithm’s HEAD layer. The study results show that, with only 10% of the data processed, a mean Average Precision (mAP) of 0.70±0.04 is achieved on the dataset,corresponding to a mAP gain of 5.14% compared to traditional semi-supervised object detection techniques. The study findings provide a theoretical reference for future research on object detection large models in the field of rail transit.