EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

Addressing the challenge of surface defect detection in load-bearing rails within auto-motive assembly workshops, which operate in complex environments and under long-term service, this paper proposes an innovative detection framework based on an improved YOLOv5 network. This framework, designed specifically for the unique challenges presented by load-bearing rails, integrates advanced machine vision and deep learning technologies. Initially, a Multi-Scale Pyramid Pooling (MSPP) module, incorporating the concept of residual stacking, is introduced to effectively enhance the extraction of complex features; Subsequently, the coordinate attention mechanism is optimized, leading to the development of a novel Spatial Coordinate Attention Mechanism (DAM), focused on detecting small-sized defects; Thereafter, a Dual Sampling Transition Module (DSTM) is applied to enhance information retention during the down-sampling process; Finally, the DBDAMN clustering algorithm is utilized to optimize anchor sizes, allowing for more precise adaptation to the diversity of defect sizes. These innovations significantly improve the accuracy of surface defect detection in load-bearing rails, particularly in identifying small defects, offering an effective means of preventing workshop safety incidents. The experimental results demonstrate that this method achieves 97.3% on AP50, marking a 4.2% improvement over the standard YOLOv5 model, thus indicating a significant performance enhancement. To validate the superiority of our model, a comparison with popular current models was conducted, achieving optimal values in recall rate, accuracy, and mAP, which were 91.4%, 92.6%, and 88.9%, respectively. Therefore, the proposed method meets the requirements for precision in rail defect detection.