## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.3.3 Experimental Comparison of the Optimization Mechanism  
To effectively analyze the performance of the improved model, the original and CCA-YOLO networks were trained using the same training parameters and methods. To strengthen the ability of the YOLOv5s model to extract spatial information features, this study focused on redesigning the feature extraction network of YOLOv5s. Module A adds  
Figure 7: Comparison of loss curves of different loss functions. (**a**) Tear detection. (**b**) Scratch detection. small-target feature detection, and module B introduces the CCA mechanism. Module C replaces CIoU with the \(\alpha\)-IoU loss function to improve the scratch detection accuracy, and module D replaces CIoU with EIoU to improve the tear detection accuracy. The aim of this ablation comparison experiment is to verify the optimization of each improvement module. From the experimental data in Table 3, it can be seen that under the same experimental parameters, if the three types of datasets, vertical angle, horizontal angle, and scratches, are placed in the same model for training, the mAP value is lower than 80%, and the detection effect is poor. If the three models are trained separately and independently, the mAP values are higher than 90%. Meanwhile, according to the experimental results, it is found that, based on the different features of tear and scratch, the introduction of EIoU to detect glove tear defects and the introduction of \(\alpha\)-IoU to detect glove scratch defects can improve the two-week defect detection accuracy, and the detection effect is better. Table 4 shows the dataset acquired at a vertical angle and generated under the condition of the same training parameters for 100 epochs. As shown in Table 4, the aforementioned changes improved the detection of small scratch targets in nitrile gloves. In particular, mAP increased by 3.5% after the addition of the small-target detection layer, by 1.6 percentage points after the addition of the CCA modules, and by 2.5 percentage points after the use of the \(\alpha\)-IoU loss function. After using the EIoU loss function, mAP increased by 1 percentage point. According to the data analysis, the improved module positively affects the feature extraction capability with a small \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP** & **Param** & **GFLOPs** \\ \hline YOLOv5s & 93.8 & 90.0 & 90.1 & 70.6 & 16.4 \\ YOLOv5s + A & 92.8 & 94.9 & 93.6 & 72.5 & 16.8 \\ YOLOv5s + B & 94.1 & 94.8 & 91.7 & 74.3 & 16.9 \\ YOLOv5s + C & 94.1 & 93.5 & 92.6 & 70.6 & 16.4 \\ YOLOv5s + D & 93.9 & 91.1 & 91.1 & 70.6 & 16.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The data sheet of ablation experiment 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** \\ \hline YOLOv5s & 93.8 & 90.1 & 90.1 \\ SE & 94.0 & 91.1 & 91.7 \\ CBAM & 92.8 & 91.1 & 90.9 \\ ECA & 92.5 & 91.2 & 90.2 \\ CA & 97.3 & 90.0 & 91.7 \\ CCA (replace) & 92.7 & 93.7 & 92.0 \\ CCA (add) & 94.3 & 96.1 & 92.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The data sheet of ablation experiment 2.