# Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly
## 4 Discussion  
In response to issues such as incorrect assembly, missing components, and low efficiency of manual inspections in the automotive wiring harness relay assembly process, a vision inspection system based on an improved YOLOv5s model was designed. The enhanced YOLOv5s model used in this study significantly improved the capability to recognize types, colors, and characters of relays in motion scenarios. This enhancement has led to improved assembly quality and reduced labor intensity. This section focuses on discussing the research results, potential limitations of this study, and suggestions for future research. In this paper, the AFF_Detect module replaces the original Detect module of YOLOv5s, making full use of feature information at different scales to address the issue of lost target feature information. By adaptively fusing deep and shallow features, the overall performance of the model is enhanced. To improve the model's understanding of global relationships and expand its perceptual range, this paper integrates the GCNet with the C3 module, proposing using the C3 GC module to replace the original C3 module. This effectively increases the model's detection accuracy. Finally, to meet the criteria for industrial  
Figure 10: Detection effect comparison. (**a**) is the original image, (**b**) is the detection result of YOLOv5s, (**c**) is the detection result of YOLOv5s-ASGG. applications, GSConv is used in place of the original Conv method in the Neck, reducing the model's parameter count and increasing detection speed. The experimental results demonstrate that the use of ASFF_Detect achieved an accuracy of 98%, which is a 0.4% increase compared to YOLOv5s; the mAP reached 98.9%, showing a 0.2% improvement; and the F1 score improved to 98.45, an increase of 0.46. This indicates that ASFF_Detect can comprehensively enhance network performance. Building upon this, the use of C3 GC achieved an even higher accuracy of 99.3%, which is a 1.7% improvement over YOLOv5s; the mAP increased to 99.3%, up by 0.6%; and the F1 score rose to 99.3, an increase of 1.31. These results suggest that YOLOv5s-ASG has the best performance, significantly enhancing the baseline model. With the replacement of the Neck Conv with GSConv, the accuracy was 99.2%. Although this is a slight decrease of 0.1% compared to YOLOv5s-ASG, the model's parameter count and size were reduced by 2% and 3.8%, respectively. Therefore, YOLOv5s-ASGG achieves a reduction in model size and computational load with a minimal decrease in accuracy, further enhancing the overall performance of the network. The algorithm proposed in this paper is thoroughly compared with the original YOLOv5s algorithm and other mainstream algorithms. While YOLOv3 performs well in multi-target detection tasks, its accuracy is relatively low when dealing with small targets, such as automotive wire harness relays. On the other hand, although YOLOv5s provides high accuracy as a large model in the YOLOv5 family, it does not perform as well as the improved YOLOv5s algorithm in this paper in terms of real-time performance, especially in environments with limited hardware resources. In addition, YOLOv7 and YOLOv8n, as new advances in the YOLO family, demonstrate excellent detection speed and accuracy. However, in specific automotive harness assembly scenarios, the improved YOLOv5s-ASGG algorithm in this paper provides a more suitable solution through targeted optimization. The YOLOv5s-ASGG algorithm shows superior performance in key metrics such as mAP, P and R. Compared with the original YOLOv5s algorithm, the mAP of YOLOv5s-ASGG is improved by 0.5% to 99.4%, and the accuracy rate is improved by 1.6% to 99.2%. Meanwhile, the F1 scores of all types of AP values are also improved.