Research on pavement crack detection algorithm based on improved Yolov5

In order to efficiently and accurately identify and detect pavement cracks, this study proposes a pavement crack detection algorithm based on an improved Yolov5s network model. The algorithm introduces a weighted bi-directional feature pyramid network BiFPN as a neck feature network for fusing feature maps of different dimensions, thus enhancing the bottom feature information and improving the feature aggregation effect. In addition, CBAM, an attention mechanism, is employed to enhance the learning and extraction of feature information from the pavement crack image, while attenuating the influence of the pavement background, which is similar to the crack, on the detection results. The experimental results on the homemade dataset show that the improved yolov5s model improves 10%, 2.2%, and 4.6% over the original model in terms of precision, recall, and mean average precision values, respectively. This indicates that the improved algorithm is feasible for pavement crack inspection.