## 4 Experimental Design and Results Analysis
### Experimental Results and Analysis
#### 4.4.3 Final Model Performance Analysis
After adding the CGC2f lightweight network, the ESE module and the DyHead module were added, and the mAP50 increased by 1.4%, and the obstacle detection accuracy was improved, but the speed dropped from 207.3 FPS to 172.4 FPS. In the actual application of obstacle detection in open-pit mines, such a combination is reasonable and necessary. The complexity of the open-pit mine environment requires the model to not only be able to quickly detect obstacles but also to ensure the accuracy of detection. The combined module performs well in improving the detection accuracy and can more accurately identify obstacles of different types, scales  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CGC2f** & **ESE** & **DyHead** & **LAMP** & \begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular}  & **GFLOPs** & **FPS** & **mAP50 (\%)** \\ \hline \(\times\) & \(\times\) & \(\times\) & \(\times\) & 11.1 & 28.5 & 177.8 & 94.2 \\ ✓ & \(\times\) & \(\times\) & \(\times\) & 9.3 & 23.2 & 207.3 & 94.6 \\ \(\times\) & ✓ & \(\times\) & \(\times\) & 11.5 & 28.5 & 175.3 & 94.8 \\ \(\times\) & \(\times\) & ✓ & \(\times\) & 10.9 & 28.3 & 158.7 & 95.2 \\ ✓ & ✓ & \(\times\) & \(\times\) &and environmental conditions. For example, when there is large equipment, scattered ores, pedestrians, etc., on the mine road at the same time, the combined module can fully and accurately detect these targets, providing a more reliable decision-making basis for the safe driving of vehicles. Although the speed has decreased, this trade-off is acceptable in some scenarios where the accuracy requirements are extremely high, and the speed requirements are relatively less stringent, such as vehicle detection in specific operating areas inside mines. At the same time, we are also aware of the potential problems of latency-sensitive systems (such as high-speed open-pit mining vehicles), so we subsequently used the LAMP method to optimize the model, which greatly improved the speed while ensuring accuracy (compared with the original YOLOv8s model, the model complexity was reduced by 21 GFLOPs, the speed was increased by 71.5 FPS, and the mAP50 was increased by 1.1%), so as to better meet the comprehensive requirements of accuracy and speed in different driving scenarios in open-pit mines. Under the same experimental conditions, the proposed algorithm was compared with other target detection algorithms, and the comparison results are shown in Table 7. The proposed algorithm was compared with the two-stage detector Faster R-CNN, and both showed higher accuracy, but the two-stage process added additional computational burden. In terms of single-stage detectors, RetinaNet [29] has good small object detection performance but has high computational cost during training and inference. SSD [30] has better detection results, but its speed is relatively slow due to its multi-scale prediction and the generation of a large number of prediction boxes. The performance of the proposed algorithm was compared with the current YOLO series network. YOLOv4-tiny [31] has a significant advantage in terms of calculation amount, but its feature extraction network has relatively few layers, and its ability to express complex features is limited. It is difficult to fully learn sufficiently rich and detailed feature information, which makes it prone to bias when distinguishing similar targets, resulting in a high miss detection rate. Although YOLOv3-tiny, YOLOv7-tiny, and YOLOv8n show high inference speeds, their recall rate and accuracy are lower than the proposed algorithm. For YOLOv3-tiny, in order to achieve lightweight and high inference speed, the network is over-simplified, and the feature extraction layer is reduced, resulting in insufficient learning of complex target features in open-pit mines. YOLOv7-tiny has been optimized to achieve good inference speed, but the feature fusion could be better. Although YOLOv8n takes into account both speed and performance, it needs to be more accurate in configuring the depth and width of the feature extraction network.