## 3 Implementation Details and Experimental Results
### Comparison of Network Improvements  
The baseline model utilizes the YOLOv5s network structure. The ECA module is incorporated into the Bottleneck structure based on the baseline model, optimizing feature transfer and reuse by fusing the ECA mechanism with the Bottleneck module, a modification we refer to as Baseline + ECA. The YOLOX network's decoupled head structure also replaces the YOLOv5s's coupled head structure and is called Baseline + DH. Concurrently, the CIoU localization loss is substituted with GIoU and is designated Baseline + GIoU. These three enhancements collectively form Baseline + GIoU + ECA + DH. This study conducted comparative experiments on datasets with fine-crack annotations to evaluate the efficacy of these improvements. The weight of the localization loss was adjusted in \begin{table}
\begin{tabular}{c c} \hline \hline
**Configured Contents** & **Type** \\ \hline Operating System & Linux \\ CPU & Xeon(R) Platinum 8352V \\ GPU & NVIDIA GeForce RTX 4090, 24G \\ Pytorch & Version 2.0.1 \\ CUDA & Version 11.7 \\ cuDNN & Version 8.5.0 \\ Python & Version 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of the deep learning computing environment. Figure 8: Experimental process and site. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Configured Contents** \\ \hline UAV type & DJI mavic3 \\ Microcomputer & Raspberry Pi 4B \\ Camera frame rate & 30 fps/s \\ Camera pixels & 4 million pixels \\ Maximum camera resolution & 2688 \(\times\) 1520 \\ Binocular camera baseline & 70 mm \\ Camera focal length & 3.0 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the image acquisition device. the loss function to improve the recall of crack detection. The training process is shown in Figure 9. The proposed model had the highest recall and the lowest loss value throughout the training process. As shown in Table 4, the baseline model's mean Average Precision (mAP) for crack detection tasks was 81.02%. Implementing the ECA mechanism at critical points within the network structure reduced the model's parameters and adeptly captured inter-channel dependencies through a localized cross-channel interaction strategy. This integration of the ECA module elevated the mAP to 82.82%, underscoring the mechanism's effectiveness in augmenting the model's crack feature recognition capabilities. Furthermore, introducing the YOLOx decoupled head structure refined the network training by distinctly separating the classification and localization tasks. This separation resulted in enhanced focus and precision, leading to a 2.10% increase in the mAP and an improvement in recall to 82.56%. By converting the bounding box loss function to GIoU loss, which accounted for both the coverage and the distance between bounding boxes, the model gained more comprehensive crack localization feedback, further raising the mAP to 82.33%. The combined application of these three improvements significantly boosted the network's recall to 86.82% and boosted the mAP to 86.32%. These data emphatically demonstrate that the strategic enhancements to the YOLOv5s architecture improve its capability to detect pavement crack defects. Figure 10 exemplifies the results of pavement crack detection. The initial YOLOv5s model, prior to improvement, exhibited missed detections, particularly in areas with long cracks. The enhanced model demonstrated improved recall, comprehensively covering the cracks with small boxes and exhibiting higher confidence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** \\ \hline YOLOv5s baseline & 82.57 & 80.57 & 81.02 \\ YOLOv5s + ECA & 80.66 & 83.68 & 82.82 \\ YOLOv5s + decoupled head & 86.41 & 82.56 & 83.12 \\ YOLOv5s + GIoU & 82.12 & 85.55 & 82.33 \\ YOLOv5s + ECA + GIoU + decoupled head & 85.96 & 86.82 & 86.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results of network improvements.