## 4 Experiments
### Experimental Results
GC-YOLOv5s outperforms YOLOv3-tiny by 26.8%, 6%, 19.5%, 21.7%, and 32.2% in five metrics, including Precision, Recall, mAP_0.5, mAP_0.5:0.95, and _Params_, but with a 3.8 unit increase in model volume. GC-YOLOv5s outperforms YOLOX-s by 3.5%, 5.4%, and 2.7% in three metrics, including Precision, mAP_0.5, and mAP_0.5:0.95, additionally by a bigger margin in _Params_, which decreases the original model by 27% while lowering model volume by 4.8 units. GC-YOLOv5s outperforms YOLOv7 in all six metrics, with lesser increases in three metrics, Recall, mAP_0.5, and mAP_0.5:0.95, with a larger improvement in Precision of 7.6%, and a much lower number of parameters and model volume. When compared to YOLOv7-tiny, GC-YOLOv5s has a 6.2%, 1.2%, 4.2%, 5.5%, and 2.5% improvement in five metrics, including Precision, Recall, mAP_0.5, mAP_0.5:0.95, and _Params_; however, the model volume has grown by 3.6 units. In summary, as compared to the bigger YOLOv3 and YOLOv7 models, the GC-YOLOv5s achieves higher results in all metrics and has significant advantages in terms of parameter number and model volume. GC-YOLOv5s outperforms YOLOv3-tiny, YOLOv3-tiny, YOLOv7-tiny, and other models with comparable volumes in all metrics while outperforming the models in terms of the number of parameters. These findings demonstrate the method's significant promise in the field of UAV crack detection and give strong data to support its practical implementation. To test the target detection effect of the algorithms proposed in this study in real scenes, some complex and representative images from the UMSC dataset are chosen for detection, and the detection effect is shown in Figure 7, which includes the detection result of the algorithms in this paper and YOLOv3, YOLOv3-tiny, YOLOX-s, YOLOv7, and YOLOv7-tiny, respectively. The first column is the Ground Truth image, and each column after that is the detection result graph of each technique. As shown in Figure 7a, YOLOv3, YOLOv3-tiny, and YOLOX-s have false detection problems due to light interference from external environmental objects in the scene, whereas the algorithm of this paper and YOLOv7 and YOLOv7-tiny do not; however, the accuracy of this paper's algorithm for crack detection is higher than that of YOLOv7 and YOLOv7-tiny. The image contains several cracks with distinct extension directions, as seen in Figure 7b, as well as a lot of noise. YOLOv3 and YOLOv7-tiny have the problem of missed detection, and YOLOv3-tiny and YOLOX-s have the problem of false detection; only the model in this paper and the model of YOLOv7 have the more accurate detection effect; however, the effect of this paper's algorithm is also better than the YOLOv7 algorithm, as shown in Figure 7c,d. Figure 7c has the least amount of environmental interference, and none of the algorithms have missed detection or false detection issues. However, for models \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UAV Detection** & \multicolumn{6}{c}{**Performance Metrics**} \\ \cline{2-7}
**Model** & **Precision** & **Recall** & **mAP\_0.5** & **mAP\_0.5:0.95** & _Params_ & _Flops_ \\ \hline YOLOv3 & 0.748 & 0.689 & 0.695 & 0.396 & 61,497,430 & 154.5 \\ YOLOv3-tiny & 0.528 & 0.638 & 0.548 & 0.229 & 8,669,876 & 13.0 \\ YOLOv7-tiny & 0.734 & 0.698 & 0.689 & 0.419 & 8,040,626 & 21.6 \\ YOLOv7 & 0.693 & 0.694 & 0.718 & 0.433 & 36,479,926 & 103.2 \\ YOLOv7-tiny & 0.707 & 0.686 & 0.701 & 0.391 & 6,014,988 & 13.2 \\ GC-YOLOv5s & 0.769 & 0.698 & 0.743 & 0.446 & 5,873,918 & 16.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results of various models. such as YOLOv3-tiny, YOLOv7, and YOLOv7-tiny, the light reflection problem in Figure 7d leads to the problem of the repetitive detection bounding box. The algorithms in this study outperform other YOLO-type techniques in terms of crack-detecting accuracy. In conclusion, the GC-YOLOv5s algorithm is capable of better overcoming the influence of the complex environment and achieving accurate identification of multi-scale cracks.