# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 4 Experiments
### Performance Comparison  
In this chapter, we conducted a comparison using two approaches. The first approach involved comparing the improved R-YOLO v5 with other rotation detection models such as SASM, S2A Net, and Re Det in terms of predictive performance. The second approach involved contrasting the performance of the enhanced algorithm with the original algorithm. (1) Comparison of Rotational Object Detection Algorithms  
To assess the detection performance of the R-YOLO v5 network on concrete structure cracks, the same dataset of concrete structure cracks was used to train rotational object detection algorithms, including SASM [43], S2A Net [44], Re Det [45], and R-YOLO v5. Following the training, the models were evaluated using the test set for performance comparison (all comparative algorithms were executed in the same environment). The performances of SASM, S2A Net, Re Det, and R-YOLO v5 are summarized in Table 2:  
According to the results in Table 2, it can be observed that R-YOLO v5 achieves a higher [EMAIL_ADDRESS] of 94.03 compared to the other three algorithms, indicating a significant improvement in detection accuracy. The model's memory footprint is 4.17 MB, which is notably smaller than the other algorithms. Additionally, the average detection time for a single image is only 0.01 s. For detecting concrete cracks with large aspect ratios, R-YOLO v5 demonstrates superior overall performance. It meets the precision requirements while also offering advantages such as lightweight model size and fast detection speed. This makes it suitable for real-time crack detection on compact devices such as smartphones or drones. (2) Comparison of YOLO v5  
To analyze the performance differences between the R-YOLO v5 rotation-based object detection algorithm and the standard YOLO v5 object detection algorithm for detecting \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epoch** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **Precision** & **Recall** & **F1** & **Loss** \\ \hline \(1\) & 0.3741 & 0.1032 & 0.4617 & 0.4332 & 0.4470 & 0.2017 \\ \(50\) & 0.9402 & 0.5973 & 0.9215 & 0.8881 & 0.9045 & 0.0977 \\ \(100\) & 0.9401 & 0.6111 & 0.9133 & 0.9122 & 0.9127 & 0.0935 \\ \(150\) & 0.9413 & 0.6134 & 0.9174 & 0.9034 & 0.9103 & 0.0895 \\ \(200\) & 0.9412 & 0.6123 & 0.9131 & 0.9073 & 0.9102 & 0.0866 \\ \(250\) & 0.9402 & 0.6104 & 0.9163 & 0.9048 & 0.9105 & 0.0843 \\ \(300\) & 0.9403 & 0.6103 & 0.9152 & 0.9054 & 0.9103 & 0.0839 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training results of R-YOLO v5 mode. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **[EMAIL_ADDRESS]** & **Model Size (Mb)** & **Inference Time (Per Picture)** \\ \hline SASM & 80.29 & 276 & 0.22 s \\ S2A Net & 78.17 & 291 & 0.25 s \\ ReDet & 89.45 & 233 & 0.47 s \\ R-YOLO v5 & 94.03 & 4.17 & 0.01 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of rotation-aware object detection algorithms. concrete crack structures, a total of 2036 concrete crack images were annotated using original rectangular bounding boxes and then fed into the YOLO v5s network for training (under the same training environment). Finally, the trained models were evaluated using the test dataset. After conducting the tests, it was found that the YOLO v5 object detection algorithm achieved an [EMAIL_ADDRESS] of 72.12 for detecting concrete structure cracks. The model size was measured at 4.14 Mb, and the average detection time per image was 0.01 s. Comparing the test results, it is evident that R-YOLO v5 outperforms YOLO v5 with a 30.38 higher [EMAIL_ADDRESS]. Additionally, the model size of R-YOLO v5 is slightly larger by 0.03 Mb compared to YOLO v5, while the average detection time per image remains the same. Precision and F1-score are shown in Figure 10. As seen from the figure, the Precision and F1 scores of the R-YOLO v5 model are significantly higher than those of the YOLO v5 model, and it converges faster, which converged in the 10 th epoch, while the YOLOv5 algorithm converged in the 50 th epoch.