TrainNet for locking state recognition of side door of railway freight car - Ning Xing; Cai Kangcheng; Zhou Shumin; Lan Xiangui; Lai Yihui (International Journal of Advanced Robotic Systems)
Locking state recognition is one of the key tasks of railway freight monitoring. However, accurate localization and recognition of small locking mechanisms remain major challenges. Current approaches that focus on existing object recognition methods lead to high false detection and miss rates. This paper introduces TrainNet for efficient locking state detection for Open-top wagon doors. A dataset was collected using a robot with a camera to validate our method. We designed an efficient layer aggregation network (ELAN)-S module in our TrainNet, which can be used with YOLOv7. The module efficiently extracts curvilinear features and is integrated into the backbone feature extraction network to enhance the feature representation capability. An LSKCSPC module is also introduced to capture a dynamic receptive field, enabling TrainNet to adjust its receptive field dynamically to the scale of the object, improving its feature representation capacity. Furthermore, the detection head for small-scale objects is redesigned, the feature layer size is increased to enhance the ability to extract and detect fine-grained features. Finally, the loss function is modified to a dynamic fusion-based CNIOU, which reduces the sensitivity of original loss to small objects and improves performance. Experimental results show that the algorithm achieves a mean average precision (mAP50) of 91.5%, which is a 3.6% improvement over the baseline YOLOv7 algorithm. The model weight file size of new algorithm is 60.2MB with 19.5% reduction compared to the baseline. The proposed method achieves 91.5% for detecting the lock status of railway freight open-top wagon side doors, while reducing the complexity of the original algorithm and achieving a real-time detection speed of 39.8fps, meeting the requirements for practical application. The algorithm also exhibits good robustness as demonstrated by experiments on the WiderPerson dataset.