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Abstract

Railways are developing rapidly in countries worldwide due to their efficient transportation capacity. Accurate detection of train underbody bolts is crucial to railway safety. However, current bolt detection methods have low accuracy and high computational complexity. At the same time, the complex bolt detection background affects the classification and positioning performance. To address these challenges, a computer vision detection algorithm ERBW-Bolt which can accurately detect bolts is proposed in this paper. Based on the YOLOv8 algorithm, first, the EMAC2f module with attention mechanism is introduced in the backbone network to enhance the feature extraction capability. In addition, the neck network is improved by Re-parameterized Bidirectional Feature Pyramid Network (RepBIFPN) to enhance the feature fusion capability and computational performance of the network. Finally, to improve the model’s ability to balance samples of different qualities, the bounding box loss function is replaced by Wise Intersection over Union (WIoU). We conducted various experiments on the train underbody bolt dataset. Experimental results show that compared with the YOLOv8, the mAP50 of ERBW-Bolt is improved by 2.6% to 85.8%, the mAP50:95 is improved by 2.5% to 55.9%, the FPS (Frames Per Second) is improved by 84.9 to 107.8 f/s, and the Parameters is reduced by 3.7M to 7.43M, which greatly improved detection accuracy and computational efficiency. Compared with Faster R-CNN (region-based convolutional neural network), SSD (single shot multibox detector), YOLOv5, YOLOv9, YOLOv10, and YOLOv11, the ERBW-Bolt achieves higher detection accuracy and computational efficiency. Compared with detection transformers (DETR) and real-time detection transformers (RTDETR), the ERBW-Bolt achieves a better balance between detection accuracy and computational efficiency. Overall, the ERBW-Bolt model satisfied the requirement for detecting train underbody bolts in complex backgrounds, which is of great significance to the detection and maintenance of train underbody bolts.

Keywords

Bolt detection attention mechanism feature pyramid network bounding box loss function

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ERBW-bolt: A high-accuracy model for train underbody bolts detection in complex background

Abstract

Keywords

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