# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
## 5 Conclusions  
Through comprehensive research, the bridge apparent disease detection algorithm is improved. Through self-made enhanced dataset test experiments, ablation experiments, visual effect detection and comparative result analysis, the following key conclusions are drawn:  
1. In response to the problems posed by the diverse target types, variable morphological characteristics, numerous small sample targets, and a high likelihood of missed detections in bridge surface diseases, this study replaced the ELAN-Tiny module of the original YOLOv7-Tiny algorithm with a DBB module. Additionally, the traditional CIoU loss function had been replaced with a boundary box regression loss function based on MPDIoU. Based on this foundation, the improved YOLOv7-Tiny-DBB detection algorithm for the identification of surface defects in bridges had been proposed. This approach not only enriched the extraction of feature information but also enhanced regression prediction capabilities, effectively addressing the issue of missed detections encountered with the YOLOv7-Tiny algorithm.
2. The proposed improved YOLOv7-Tiny-DBB detection algorithm was effectively trained and tested using a self-constructed augmented dataset. The results indicated that the modified algorithm achieved an increase of 4.2% in precision, 6.5% in recall, 5.4% in F1 score, and 7.3% in mean Average Precision (mAP) compared to the original YOLOv7-Tiny algorithm. Additionally, the detection speed improved by 13.1 FPS, and further validation through ablation experiments confirmed the efficiency and effectiveness of the proposed improvements.
3. The improved YOLOv7-Tiny-DBB algorithm demonstrated a significant reduction in both the number of parameters and floating-point operations, resulting in enhanced detection speed and performance. Visualization tests indicated that the application of this improved algorithm effectively mitigated the risk of detecting the reinforcement  
Figure 13: Comparison of the algorithm detection effects before and after improvement. (**a**) Exposure of reinforcement disease; (**b**) Common crack disease; (**c**) Microcracks disease.  
exposure defects and the microcracks defects. This advancement provided a novel approach for deploying the real-time detection of surface diseases on bridges using industrial edge devices.
4. In future research, it is essential to further enhance the construction of datasets that capture the distribution of bridge defects under various challenging conditions. Additionally, comparisons and improvements with more advanced network models should be conducted to increase the effectiveness of this method. Building on this foundation, experimental studies on real-time detection of the apparent bridge defects using edge devices will be initiated.  
H.A.: Conceptualization, Data curation, writing--review and editing. Y.F.: Investigation, Methodology, Validation. Z.J.: Formal analysis, Supervision. M.L.: Methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanxi Science and Technology Project (201903D121176) and the Shanxi Province Youth Fund Project (202203021212306).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.