Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB

In response to the diverse target types, variable morphological characteristics, and the prevalence of small sample targets that are prone to missed detections in the bridge surface disease identification, this paper proposes an improved algorithm for detecting bridge surface diseases based on YOLOv7-Tiny-DBB. By introducing the DBB module to replace the ELAN-Tiny module in the backbone network, the capability of multi-scale feature extraction during the training phase is enhanced, the number of parameters during inference is reduced, and the inference speed has been accelerated. Additionally, by substituting the CIoU loss function with a boundary box regression loss function based on MPDIoU, the regression prediction capabilities are strengthened and both regression accuracy and speed are improved. Effective training and testing are conducted using a self-constructed augmented dataset. The results indicate that, compared to the YOLOv7-Tiny algorithm, the improved algorithm achieves an increase of 4.2% in precision, 6.5% in recall, 5.4% in F1 score, and 7.3% in mean Average Precision (mAP). Additionally, the detection speed improves by 13.1 FPS, successfully addressing the issue of missed detections for minor diseases. The ablation experiments, along with the performance comparison of different network models and visual effect assessments further corroborate the effectiveness of the proposed improvements, providing critical technical support for the deployment of real-time detection systems for bridge surface diseases on industrial edge devices.