# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
[PERSON] [15] proposed the YOLOv4-fpm deep learning model for bridge crack detection, building upon the YOLOv4 framework. The pruning algorithm to simplify the network was employed and the loss function using focal loss was optimized, resulting in a reduction of model size and parameters by 18.2%. The YOLOv7 algorithm was improved by integrating the SimAM and CARAFE attention mechanisms. This enhancement had resulted in a 15% reduction in inference time for defect detection in large-span bridges [16]. [PERSON] [17] proposed the BGV-YOLOv5 bridge surface irregularity detection system, taking into account factors, such as lighting conditions, weather variations, and changes in bridge materials. This system provided significant reference for accurately monitoring abnormal situations during the operation of highway bridges. In the application of the deep learning models to edge devices, [PERSON] [18] achieved the automation of the digital meter reading process through a deep learning-driven universal controller and flow meter. The robustness of this system was validated through evaluation experiments. [PERSON] [19] focused on various architectures of deep learning and explored the potential obstacles to deploying these technologies on edge devices, thereby providing direction for future research. [PERSON] [20] introduced the convolution and ICTNet network architecture, which successfully enhanced modulation recognition accuracy while reducing model size. This approach had been effectively applied to signal modulation recognition in resource-constrained small devices within Internet of Things (IoT) environments. [PERSON] [21] developed an electric vehicle violation detection system for unauthorized passenger transport, based on an improved YOLOv5 network model. This system incorporated a lightweight MobileNetV3 architecture and the ECA-Net attention mechanism. The enhancements resulted in an 18% reduction in the number of parameters. [PERSON] [22] developed a bridge monitoring module based on edge computing technology, which addressed issues, such as insufficient bandwidth, high energy consumption, and data security associated with cloud computing, to some extent. In summary, both domestic and international scholars have achieved significant results in the detection of surface diseases on bridges and the deployment of edge devices using deep learning-based object detection algorithms. These findings provide important references for the development of this research. However, the inherent complexity of bridge diseases and their backgrounds somewhat limits the improvement in detection accuracy. Some high-precision recognition models possess a large number of convolutional layers and parameters, resulting in larger model sizes and slower detection speeds. Additionally, these models are prone to missing detections, making them unsuitable for deployment on edge devices. To this end, this paper conducts an analysis based on the principles of the YOLOv7-Tiny algorithm. It replaces the ELAN-Tiny module in the original backbone feature extraction network with a multi-scale feature extraction module known as Diverse Branch Block (DBB). This modification not only enriches the extracted feature information but also enhances model parameter capacity, accelerates inference speed, and improves multi-scale object detection performance. The loss function CIoU is replaced with MPDIoU to enhance the regression prediction capability. Consequently, an improved algorithm for detecting surface diseases in bridges based on YOLOv7-Tiny-DBB is proposed. The effectiveness of this improvement is further validated through training and testing on a self-constructed augmented bridge surface dataset, as well as verification and visualization of results. This research provides critical technical support for the accurate and rapid identification of surface diseases in bridges, facilitating the deployment of real-time detection equipment at industrial edges.