## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.2 Pruning Results and Analysis  
The improvement has improved the accuracy of the network and reduced the number of network parameters and calculations. However, due to the enhanced perception of  
Figure 12: Model performance indicators: (**a**) precision; (**b**) recall; (**c**) mAP50; (**d**) mAP50-95; (**e**) train/box_loss; (**f**) val/box_loss. the head network, the amount of memory access has increased. In order to eliminate this effect and improve the pace at which network obstacle detection, the improved YOLOv8s is pruned using the LAMP score method. Before pruning, it is necessary to perform a layer-skipping operation because there may be some problems in the network that cannot be pruned. The improved network's output layer must be skipped because it is in charge of transforming the feature map processed by many layers into the final detection result, which includes the target's location, category and confidence. If it is pruned, the output information will be lost, so this layer needs to be skipped. Experiments have found that when the CGC2f module of the improved backbone network is pruned, an error will occur when it reaches a certain layer. Since the CGC2f module is used for feature extraction and conversion, it contains convolutional layers, Bottleneck modules, etc., to extract the features of the input data and perform corresponding processing. If it is pruned, the stability of the structural extraction features will be destroyed, resulting in reduced performance. Therefore, it is necessary to perform a skip operation on this layer. For the ESE attention module, the pruning process is made easier and more efficient by simplifying the structure, reducing the computational burden, allowing adaptive feature extraction, and optimizing the model size, so there is no need to perform a skip operation on the ESE module. After completing the preparatory operations before pruning, the network is pruned to different degrees by adjusting the Speed-up parameter. The pruning effect is shown in Table 5. Speed-up indicates the improvement ratio of the speed at which the pruned model is compared with the model before pruning. When Speed-up is 1, it indicates the model without pruning. As shown in Table 5, when Speed-up is 1.5 and 2, the speed increases by 17.7 FPS and 34.4 FPS, respectively, and the speed increase effect is not obvious. When the Speed-up is 2.5, the speed increases by 42 FPS, but compared with a Speed-up of 3, mAP decreases more, and FPS is lower. When Speed-up is 3.5, 4, 4.5, and 5, although FPS increases, the obstacle detection accuracy is greatly affected. In summary, when the Speed-up is 3, the speed increases by 76.9 FPS, and mAP decreases by 0.3% compared with before pruning, which significantly boosts speed while guaranteeing obstacle detection accuracy. Finally, the pruning operation corresponding to a Speed-up of 3 is selected. As shown in Figure 13, the layers of the pruning channel when Speed-up is three under the LAMP strategy are shown in the display diagram before and after pruning, which can intuitively show the redundancy of the network structure. \begin{table}
\begin{tabular}{c c c c c} \hline
**Speed-Up** & **FPS** & **GFLOPs** & **Model Size (M)** & **mAP50 (\%)** \\ \hline
1 & 172.4 & 22.7 & 18.3 & 95.6 \\
1.5 & 190.1 & 15.1 & 12.2 & 95.5 \\
2 & 206.8 & 11.3 & 9.3 & 95.3 \\
2.5 & 231.3 & 9.0 & 7.4 & 94.9 \\
3 & 249.3 & 7.5 & 6.3 & 95.3 \\
3.5 & 258.3 & 6.4 & 5.5 & 94.2 \\
4 & 267.7 & 5.6 & 4.8 & 93.9 \\
4.5 & 277.4 & 5 & 4.4 & 93.0 \\
5 & 280.5 & 4.5 & 4.0 & 92.6 \\ \hline \end{tabular}
\end{table} 
Table 5: Pruning effects that correspond to different pruning degrees. In order to compare the performance of the model before and after pruning, the PR curve (Precision-Recall Curve) is further analyzed. The PR curve is obtained by connecting the P-value and R-value at different confidence levels with the recall rate as the horizontal axis and the precision rate as the vertical axis.