## 4 Experiments
### Experimental Results  
Ablation tests were conducted on each module to verify the validity of each module in GC-YOLOv5s, and the results are reported in Table 2. Among them, GC-YOLOv5s-A is the version that replaces CIOU with Focal-GIOU on top of the original YOLOv5s, GC-YOLOv5s-B is the version that introduces the deconvolution on top of GC-YOLOv5s-A, GC-YOLOv5s-C is the version that introduces Ghost and C3 Ghost modules on top of GC-YOLOv5s-B, and GC-YOLOv5s is the version that adds the CSPCM module to GC-YOLOv5s-C. The results show the fact that Precision, Recall, mAP_0.5, and mAP_0.5:0.95 measures of GC-YOLOv5s-A are superior to those of YOLOv5s, which suggests that the Focal-GIOU loss function successfully reduces the problem of sample imbalance in crack images. Although the Recall and mAP_0.5:0.95 metrics decreased slightly in GC-YOLOv5s-B compared to GC-YOLOv5s-A, the Precision and mAP_0.5 metrics increased significantly, which indicates that the introduction of reverse convolution improves the model's ability to express features while also increasing the number of model parameters and the model volume. Precision, mAP_0.5, and mAP_0.5:0.95 metrics are greatly improved when compared to GC-YOLOv5s-B after the introduction of the Ghost module and the C3 Ghost module to replace the original convolutional layer and C3 module, while the number of parameters and model volume is reduced. This proves that, despite the Ghost module's use of a low-cost convolutional operation, the operation of shedding redundant features resulted in a superior feature representation of the model. In this research, the suggested model (GC-YOLOv5s) outperformed the GC-YOLOv5s-C in terms of Precision and mAP_0.5:0.95. However, Recall and mAP_0.5:0.95 decreased slightly, which can be attributed to the variable responsiveness of CSPCM to specific cracks in different environments. Nevertheless, the addition of CSPCM results in an 8.2% reduction in the original parameter count and a 0.6 reduction in model size, making the model more compact and easier to deploy. Last but not least, the ablation tests show that inserting different improvement modules can considerably increase the performance of the UAV crack-detection model. Precision, recall, mAP_0.5, and mAP_0.5:0.95 measures all improved significantly in the final experiment. These findings give strong support for the study presented in this work as well as significant insights for future UAV-crack-detection development and implementation. To further evaluate the algorithm's performance, this research conducts a comparative experiment using the same training approach, the same equipment, and the same conditions \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UAV Detection** & \multicolumn{5}{c}{**Performance Metrics**} \\ \cline{2-7}
**Model** & **Precision** & **Recall** & **mAP\_0.5** & **mAP\_0.5:0.95** & _Params_ & _Flops_ \\ \hline YOLOv5s & 0.687 & 0.707 & 0.715 & 0.415 & 7,012,822 & 15.9 \\ GC-YOLOv5s-A & 0.708 & 0.732 & 0.724 & 0.419 & 7,012,822 & 15.9 \\ GC-YOLOv5s-B & 0.734 & 0.725 & 0.741 & 0.41 & 8,323,926 & 22.5 \\ GC-YOLOv5s-C & 0.764 & 0.712 & 0.752 & 0.436 & 6,399,182 & 17.4 \\ GC-YOLOv5s & 0.769 & 0.698 & 0.743 & 0.446 & 5,873,918 & 16.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GC-YOLOv5s effect under each module. on the UMSC dataset. Six metrics, including Precision, Recall, mAP_0.5, mAP_0.5:0.95, _Params_, and _Flops_, are used to assess the effectiveness of six different sizes and types of YOLO models, including GC-YOLOv5s, YOLOv3, YOLOv3-tiny, YOLOX-s, YOLOv7, YOLOv7-tiny, and others. Table 3 summarizes the evaluation outcomes. The experimental results show that, when compared to YOLOv3, GC-YOLOv5s improves Precision and Recall metrics by 2.1% and 0.9%, respectively, while the mAP_0.5 and mAP_0.5:0.95 metrics improve by 4.8% and 5%; furthermore, the number of parameters and model volume is much better.