Research on pavement crack detection algorithm based on adversarial and depth-guided network

In recent years, deep learning algorithms, such as convolutional neural networks, have shown promising results in pavement crack detection. However, in practical engineering applications, existing pavement crack detection methods often rely on block-level crack labeling due to the challenges in producing pixel-level pavement crack labeling images. This approach is often accompanied by the difficulty of recognizing fine cracks in pavements. In this paper, we propose a method for detecting pavement cracks based on adversarial and depth-guided networks. The method consists of two components: a pixel-level pavement crack marker extraction algorithm based on edge detection, and a pavement crack detection algorithm based on adversarial and depth-guided networks (UCRGNet). The former can extract pixel-level crack markers from block-level cracks, thus effectively addressing the issues of generating pixel-level crack marker images and achieving finer marker granularity. The latter is based on the concept of generative confrontation, which improves the network's feedback to small crack regions by providing necessary supervision to the generated pavement crack segmentation images. Additionally, it incorporates a bootstrap filtering module and an attention mechanism to address the issue of information loss, thereby enhancing the model's ability to accurately identify fine cracks. The pavement crack detection method proposed in this paper is based on adversarial and depth-guided networks. It has been tested on the NCDataset dataset, and the results demonstrate that its accuracy, precision, and recall in recognizing pavement cracks are higher compared to other similar algorithms. Specifically, the method achieves an accuracy of 95.89%, precision of 67.96%, and recall of 65.93%.