## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.3 Final Model Performance Analysis  
To verify the final model's performance, mAP50, FPS, Parameters, and GFLOPs are used to evaluate it, and Table 6 displays the experiment's outcomes. First, the lightweight CGC2f network, ESE attention mechanism network, and DyHead network are added to the original YOLOv8s network model and the performance of the network is evaluated; on the basis of adding the CGC2f network, the ESE attention mechanism and the DyHead network are added in turn and their detection performance is tested; finally, the LAMP method with a Speed-up of 3 is used to prune it, and the detection performance after pruning is tested. Figure 14: Comparison of PR curves before and after pruning: (**a**) PR curve before pruning; (**b**) PR curve after pruning. Figure 13: Comparison of channels before and after pruning. As shown in Table 6, after adding the lightweight CGC2f to the original YOLOv8s network, the network speed increased by 29.5 FPS, the model complexity decreased by 5.3 GFLOPs, the number of parameters decreased by 16.2%, and the mAP50 increased by 0.4%, achieving the goal of lightweight, which helps unmanned engineering machinery to quickly and accurately detect various obstacles in open-pit mine environments. Although the speed was reduced by 2.5 FPS when the ESE module was added alone, the mAP50 was increased by 0.6%. In open-pit mine scenes, there is a large amount of complex visual information, such as ore textures under different lighting conditions, obstacles of different colors and shapes, etc. The ESE module learns and assigns channel attention weights to make the model pay more attention to feature information that is important for obstacle detection, thereby improving detection accuracy. Although this requires additional computing resources and leads to a decrease in speed, in the case of some obstacles with colors similar to the surrounding environment (such as ores scattered on the road) in driving obstacle detection, a higher mAP can ensure that these difficult-to-distinguish targets are accurately identified, even if this may slightly slow down the overall detection speed. For open-pit mine driving safety, accurate identification of such potentially dangerous obstacles is crucial to avoiding engineering accidents, so this trade-off is worthwhile. After adding the DyHead module alone, the perception ability of the head network is enhanced, the structural complexity is increased, the memory access is increased, the FPS will decrease, but the mAP50 will increase by 1%. The DyHead module is mainly dedicated to enhancing the perception ability of the head network to better handle the rich and diverse target scales and complex features in the open-pit mine scene. There are objects of different scales in the mining environment, ranging from large engineering machinery to tiny pedestrians and falling rocks. Through its unique structural design, the DyHead module can effectively integrate multi-scale feature information and improve the model's detection ability for obstacles of different scales. For example, when detecting large transport vehicles at long distances and people walking on the road at close range, the DyHead module can play an important role and significantly improve detection accuracy. Although it brings about an increase in structural complexity and an increase in memory access requirements, resulting in a decrease in speed, in complex detection scenarios such as open-pit mines that require high target scale diversity, accurate detection of various obstacles plays an irreplaceable role in ensuring driving safety. Especially in some areas with extremely high detection accuracy requirements, such as near the loading and unloading area of the mine, even if the vehicle speed is slightly reduced, it can ensure accurate identification of obstacles of various scales, thereby avoiding safety accidents caused by misjudgment or missed judgment. Therefore, the DyHead module plays an indispensable role in the model.