# Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly
## 3 Results
### Comparison of Detection Performance of Different Algorithms  
To validate the impact of ASHF_Detect and GSConv on the algorithm, ablation experiments were designed to analyze different improvements through three experimental setups. Table 1 presents a comparative experiment adding the C3 GC module, ASHF_Detect module, and GSConv to the YOLOv5s network structure. Here, YOLOv5s_AS denotes replacing the Detect module of YOLOv5s with the ASHF_Detect module. YOLOv5s_ASG indicates replacing the C3 module in the Backbone network of YOLOv5s_AS with the C3 GC module. YOLOv5s_ASGG refers to replacing the Conv module in the Neck of YOLOv5s_ASG with the GSConv module.  
The original YOLOv5s model performed on our dataset with 97.6% accuracy, 98.9% mAP and a 97.99 F1 score. By improving the model in a targeted manner, we achieve an overall improvement in performance. Specifically, the introduction of the ASHF_Detect module resulted in a 0.4% increase in accuracy, a 0.2% increase in mAP, and a 0.46 improvement in F1 scores, and the fusion of the C3 GC module on top of this resulted in another 0.5% increase in accuracy, another 0.4% increase in mAP, and a 0.85 improvement in F1 scores. These two phases of enhancement demonstrated both the ASHF_Detect module's and the C3 GC module's effectiveness in enhancing the network's feature extraction and global context understanding capabilities. Further, by replacing the Conv convolution module in the Neck with the GSConv convolution module, the number of parameters andthe size of the model are reduced by 2% and 3.8%, respectively, which effectively optimizes the computational efficiency and resource consumption of the model, even though the accuracy is slightly decreased by 0.1% to 99.2%.  
Combining all the improvements, the final network achieves an average precision (mAP) of 99.4%, an accuracy (_P_) of 99.2%, and an F1 score of 1.3, which are 0.5%, 1.6%, and 1.3%, respectively, compared to the base YOLOv5s model. These experimental results fully demonstrate that the introduction of the C3 GC module and the ASF_Detect module can significantly improve the network's detection performance in complex scenarios, while the application of GSConv achieves significant savings in model volume and computation while ensuring performance. These results validate the effectiveness and practicality of our proposed improved algorithm in motion scenario tasks such as automotive wire harness assembly error detection.  
To further validate the advantages and performance of the improved YOLOv5s algorithm proposed in this paper, a comparison was conducted with other common algorithms. Under the same experimental conditions, a performance comparison was made with classic algorithms in the field of object detection (YOLOv3, YOLOv5s, YOLOv7, and YOLOv5x). The experimental results are shown in Table 2.  
According to Table 2, the YOLOv5s-ASGG algorithm shows higher performance compared to the following five algorithms: YOLOv3, YOLOv7, YOLOv5s, YOLOv5x, and YOLOv8n. Specifically, the accuracy (P) of YOLOv5s-ASGG is higher by 3.3%, 13%, 1.6%, 1.7%, and 7%, respectively. The mean Average Precision (mAP) is higher by 0.8%, 4.2%, 0.5%, 0.3%, and 1.7%, respectively. The recall rate (R) is higher by 0.9%, 5.2%, 1%, 1.6%, and 1.8%, respectively. The F1 scores are higher by 2.11, 9.27, 1.3, 1.64, and 4.47, respectively. Overall, the performance metrics of YOLOv5s-ASGG are significantly better than those of these five algorithms.