## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.1 Improved YOLOv8s Results and Analysis
Compared with CGC2 fNet without the ESE module, P is improved by 1.3%, R is improved by 1.1%, and mAP is improved by 0.9%, which is the best improvement effect. In terms of performance, compared with SE, ESE is lightweight, reduces overfitting, accurately captures features, and is more accurate in detecting obstacles of similar appearance in open-pit mines. Compared with CBAM, in the open-pit mine scenario, ESE has slightly better accuracy and an outstanding recall rate and is more sensitive to occlusion or small obstacles. Compared with CA, ESE allocates channel weights accurately and has higher detection indicators for various types of obstacles. In terms of computational complexity, CBAM has a large amount of computation due to dual-channel processing, while ESE only has channel operations and a lightweight fully connected layer, which is actually lower and runs efficiently on mine edge devices. Compared with SE, although there are slightly more parameters, the optimization of the fully connected layer consumes less computing resources and processes data quickly. Compared with CA, ESE has a superior architecture and uses fewer resources to achieve better performance, meeting the real-time detection needs of open-pit mines. The open-pit mine environment is complex and changeable. ESE has shown strong adaptability with its intelligent learning and channel feature allocation capabilities. Compared with SE, ESE is lighter and more efficient. While improving detection capabilities, it closely meets the requirements of open-pit mine detection for accuracy and real-time performance. Compared with CBAM, ESE can focus on key information, enhance the perception of complex shape and texture obstacle features, and effectively avoid the redundancy caused by CBAM's spatial attention. Compared with CA, ESE can better capture features and adapt to environmental changes, improve generalization ability and detection accuracy, and is more applicable in open-pit mine obstacle detection systems. In summary, after the introduction of the ESE mechanism, the model has significant advantages in performance, computational complexity, and applicability in open-pit mine obstacle detection, which has effectively promoted the obstacle detection task in open-pit mine automation operations. The amount of DyHead modules connected in series determines the efficiency of the detection. Combined with the effect on the performance evaluation of the model and the computational cost, a comparative experiment was carried out on the stacking of various amounts of DyHead modules in series. As shown in Table 4, the more DyHeads are connected in series, the greater the computational complexity of the model, resulting in a gradual decrease in the model's performance. Therefore, only one DyHead module is connected in series for prediction output. To more comprehensively reflect the rationality of the improved model, the network's performance in obstacle detection during open-pit mine driving is measured by analyzing the precision, recall, mAP50, mAP50-95, and bounding box regression loss of the model before and after the improvement. Figure 12 displays the metrics for the model performance. The mAP50, mAP50-95, and recall of the improved model are higher than those of YOLOv8s. As the number of training rounds increases, the bounding box loss decreases rapidly. The improved model shows a faster learning speed, and its box_loss value decreases more \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **mAP50 (\%)** & **FPS** & **GFLOPs** & **Model Size (M)** \\
**DyHeads** & & & & \\ \hline
1 & 95.2 & 158.7 & 28.3 & 21.0 \\
2 & 94.8 & 126.7 & 28.6 & 21.9 \\
3 & 93.9 & 103.8 & 29.2 & 22.9 \\
4 & 93.7 & 87.9 & 29.7 & 23.9 \\
5 & 93.8 & 74.7 & 30.2 & 24.8 \\
6 & 93.8 & 54.4 & 30.8 & 25.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different numbers of DyHead modules. steeply, which shows that the improved model has a strong ability to capture and express bounding box annotation information.