# Leather Defect Detection Based on Improved YOLOv8 Model
## 5 Experiments and Results
### Ablation Experiments  
The YOLOVSn model serves as a benchmark to evaluate the effectiveness of each module's improvement strategy. We integrated the improved modules into the YOLOVS model and conducted ablation experiments, the results of which are presented in Table 3.  
As shown in the table, adding each of the four modules separately slightly improves the model's mAP50. In particular, when the VoV-GSCSP module was used alone, the mAP50 and mAP95 metrics improved by up to 0.86% and 1.36% compared to the original model, respectively. This indicates that the VoV-GSCSP module can effectively enhance the model's accuracy and detection performance. When EMA was incorporated individually, the model's mAP50 increased by 1.18%, but the number of Parameters (Params) \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**AFPN** & **VoV-GSCSP** & **EMA** & **Target** & **mAP50** & **mAP95** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline \(\surd\) & & & & & 0.932 & 0.734 & 3.01 & 8.1 & 66 \\  & \(\surd\) & & & & 0.937 & 0.737 & 2.59 & 8.2 & 66 \\  & \(\surd\) & & & & 0.94 & 0.744 & 2.85 & 7.3 & 70 \\  & & \(\surd\) & & & 0.943 & 0.734 & 3.05 & 8.4 & 55 \\  & & & & \(\surd\) & & 0.932 & 0.731 & 2.80 & 6.9 & 69 \\ \(\surd\) & \(\surd\) & & & & 0.941 & 0.741 & 2.89 & 8.4 & 69 \\  & \(\surd\) & \(\surd\) & & & 0.942 & 0.747 & 2.93 & 7.7 & 64 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 0.946 & 0.741 & 2.93 & 8.7 & 65 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 0.945 & 0.735 & 2.73 & 7.5 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ticked parts indicate that the corresponding modules have been added to the original model; the first row is the result of the original model, and the last row is the result of the final improved model. AFPN stands for Asymptomatic Feature Pyramid Network, VoV-GSCSP stands for modules containing Grouped Shuffle Convolution, EMA stands for Efficient Multi-scale Attention, and target refers to the shared parameter detection head. and Floating Point Operations (FLOPs) increased by 1.33% and 3.7%, respectively. This suggests that while EMA significantly improves the model's detection capabilities, it also introduces complexity to the model. Replacing the PAFPN structure with AFPN in the model's neck reduced parameters by 14.0%. Enhancing the shared parameter detection head decreased mAP95 by 0.41% compared to the original one while achieving the most significant reduction in GFLOPs by 19.75%. Incorporating both the VoV-GSCSP module and the EMA attention mechanism resulted in the model achieving its highest mAP95 of 0.747, an improvement of 1.77% over the original model. This synergy significantly enhances high-accuracy target detection. Our final model improved mAP50 by 1.39% and mAP95 by 0.14%, while reducing Params and FLOPs by 9.3% and 7.4%, respectively, compared to the original model. The improved model effectively reduced computational complexity while enhancing detection accuracy. Finally, in terms of detection speed FPS, the improved model improved 3.03% over the original model. To assess the impact of model enhancements on the detection accuracy of different defect types, Table 4 presents the detection accuracy of the YOLOV8 model, YOLOV8 with each module added separately, and the final improved model in this study for each defect type. The table illustrates that each separately added module enhances the detection of most defects. Specifically, in the case of hole defects, the GSConv module demonstrated the most significant improvement, with an accuracy increase of 0.94% compared to the original model. This confirms the substantial beneficial effect of GSConv on the detection of small targets. For scratch defects and fold defects, which are characterized by multi-scale variable aspect ratios and difficulty in being distinguished from the natural texture of leather, the introduction of EMA resulted in detection accuracy improvements of 2.13% and 2.16%, respectively, compared to the original model.