# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
## 4 Experimental Analysis and Validation
### Experimental Results and Analysis
#### 4.3.2 Analysis of the Testing Results for the Improved Algorithm
The mean Average Precision (mAP) reaches 80.6%, representing an improvement of 9.96%. Additionally, the detection speed is enhanced, making the improved algorithm particularly suitable for deployment on edge devices. In order to further evaluate the performance of the improved algorithm during testing, the classification regression results before and after the improvement are compared by analyzing the variations in the classification loss function, as illustrated in Figure 12.  
The analysis of the brown-yellow curve in Figure 12 indicates that the use of the MPDIoU loss function significantly reduces classification loss for small target objects in real-world scenarios, thereby enhancing performance. This approach effectively addresses the limitations associated with traditional CIoU loss functions, resulting in improved detection accuracy for the enhanced prediction algorithm. In order to validate the effectiveness of improvements made to the YOLOv7-Tiny-DBB algorithm, three groups of ablation experiments are designed under identical experimental conditions. The numbers in Table 4 denote the following: 1 represents the utilization of the YOLOv7 algorithm, 2 indicates the incorporation of only the DBB module, and 3 signifies \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Phase** & **Number of Layers** & **Parameter Quantity/M** & \
\begin{tabular}{c} **Floating-Point Arithmetic Numbers** \\ **(GFLOPs)** \\ \end{tabular} &
\begin{tabular}{c} **mAP** \\ **(\%)** \\ \end{tabular} \\ \hline SSD & - & - & 24.28 & - & 58.9 \\ Ref. [39] & - & - & 286.16 & - & 52.1 \\ \hline YOLOv7 & - & 415 & 37.62 & 106.5 & - \\ YOLOv7-Tiny & - & 263 & 6.23 & 13.9 & 73.3 \\ YOLOv7-Tiny-DBB & Training phase & 355 & 8.11 & 17.9 & - \\ Inference Phase & 172 & 6.01 & 13.0 & 80.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the different algorithms. the improved YOLOv7-Tiny-DBB algorithm. The results are presented in Table 4. In this table, \"\(\surd\)\" indicates the use of the improved method, while \"\(\times\)\" signifies that the improved method is not employed. According to Table 4, after applying the improved algorithm with the DBB module, the inference time for a single image is reduced by 4.7 ms, and the frames per second (FPS) increases by 12.7 FPS. This indicates an acceleration in inference speed. The DBB module demonstrates excellent feature extraction capabilities, resulting in a 6.1% improvement in mean Average Precision (mAP), thereby enhancing detection performance. Furthermore, replacing the loss function CIoU with MPDIoU led to more stable regression predictions of bounding boxes within the detection algorithm, which contributes to an increase in prediction accuracy. The mAP improves by 1.2%, indicating that the MPDIoU loss function effectively reduces instances of missed detections and enables better identification of surface defects on bridges. Additionally, the system achieves a detection rate of 59.2 FPS, which essentially meets the real-time detection requirements and further validates the effectiveness of the proposed improvements.