GBC-BCD: an improved bridge crack detection method based on bidirectional Laplacian pyramid structure with lightweight attention mechanism convolution

As roads continue to expand globally, the construction of a larger number of bridges has significantly increased the burden on bridge maintenance. Traditional methods for bridge crack detection often suffer from low efficiency and accuracy. To address these issues, we propose an efficient one-stage bridge crack detection method called GBC-BCD based on the YOLOv8n framework. To improve the model’s performance, we introduce the Coordinated Attention mechanism to enhance spatial context awareness and the Bidirectional Feature Pyramid Network to strengthen the ability of feature fusion and multi-scale feature learning. At the same time, in order to make the model more efficient while improving its performance, we introduce the Ghost Module, which significantly reduces the size of the model and raises the speed of object detection. Moreover, we employ transfer learning to improve training efficiency and conserve computational resources for small datasets. Experiments show that our model has the characteristics of lightweight and high speed. Compared with famous lightweight object detection algorithms, the model size is at most reduced by 86.6%, and realises real-time (625 FPS) processing of images (640×640), an 8.3 times increment in speed.