# 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  
In order to validate the detection performance of the improved YOLOv7-Tiny-DBB algorithm, a test set is employed for evaluation, yielding numerical values for various performance metrics, as presented in Table 2.  
Through Table 2, it can be observed that the improved YOLOv7-Tiny-DBB algorithm demonstrates a significant enhancement in detecting surface diseases of bridges compared to the YOLOv7-Tiny algorithm. The precision of YOLOv7-Tiny-DBB is 77.2%, with a recall rate of 75.5% and an F1 score of 76.3%. The mean Average Precision (mAP) reaches 80.6%. In comparison to the original YOLOv7-Tiny algorithm, these metrics show improvements of 4.2%, 6.5%, 5.4%, and 7.3%, respectively, with the most notable increase being in the mean Average Precision value. This indicates that the detection performance of the improved YOLOv7-Tiny-DBB algorithm is clearly superior to that of the YOLOv7-Tiny algorithm. Furthermore, the Average Precision (AP) metric can assess the detection accuracy of the algorithm for each category. As shown in Table 2, the detection performance for crack-related defects is the best, with an AP value of 88.0% achieved by the improved algorithm, representing a 2.5% increase compared to the original algorithm. The AP value for damage-related defects reached 80.1%, reflecting a significant improvement of 9.7%. Although this category encompasses various types, such as honeycombing, rough surfaces, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Precision** **(\%)**} & \multirow{2}{*}{**Recall** **(\%)**} & \multirow{2}{*}{**F1** **(\%)**} & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**mAP** **(\%)**} \\  & & & & & & & **Crack** \\ \hline YOLOv7-Tiny & 73.0 & 69.0 & 70.9 & 85.5 & 70.4 & 64.0 & 73.3 \\ YOLOv7-Tiny-DBB & 77.2 & 75.5 & 76.3 & 88.0 & 80.1 & 73.7 & 80.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the algorithm performance before and after improvement. Figure 11: The loss function curves during the training process of the YOLOv7-Tiny and YOLOv7-Tiny-DBB algorithms. voids, wear, and spalling of surface layers, the introduction of the DBB module enable better learning of feature information related to target objects, resulting in commendable detection performance. In contrast, regarding rebar-exposed defects--which typically occur alongside damage-related defects and exhibit considerable size variation--the AP value has increased; however, their detection accuracy remains relatively low. The performance comparison of the improved algorithm and other algorithm is presented in Table 3.  
Through Table 3, it can be observed that the Faster R-CNN algorithm required thousands of forward propagations through a convolutional neural network to perform object detection due to the extraction of over a thousand proposal regions from each image. Consequently, the amount of parameters processed is excessively large, reaching 286.16 M, which results in relatively slow processing speeds and a lower mean Average Precision (mAP) value in this experimental outcome. In contrast, the SSD algorithm significantly reduces computational load compared to the Faster R-CNN and decreases parameter count by 35.4% when compared to YOLOv7; however, its mAP value remains low and necessitates further improvements. Furthermore, compared to YOLOv7-Tiny, the improved algorithm exhibits an increase in both the number of layers and parameters during the training phase. The floating-point operations reach a total of 17.9 GFLOPS; however, this remains significantly lower than the parameter count and computational load associated with YOLOv7. This reduction can be attributed to the introduction of the multi-branch DBB module. The module also employs structural reparameterization, resulting in a 3.46% reduction in parameter count and a 6.47% decrease in floating-point operations during the inference phase.