Automatic pavement crack detection based on single stage salient-instance segmentation and concatenated feature pyramid network

Regular inspection of pavement cracks is an important task to ensure the safety of the transportation system. At present, many pavement crack detection methods still rely on the manual way. These methods are usually time-consuming and subjective. Moreover, although the automatic crack detection method has made great progress recently, there are still difficulties such as poor anti-interference ability and low detection efficiency. Therefore, this paper proposes a pavement crack detection algorithm, which can solve the above problems well. This algorithm combines single stage salient-instance segmentation (S4Net) and concatenated feature pyramid network (CFPN), which greatly improves the ability to acquire feature information. Experiments show that on the noise-free dataset, the average precision, average recall, and F1-score are 0.9331, 0.9358, and 0.9344, respectively. On the complex noise dataset, the average precision, average recall, and F1-score are 0.8244, 0.8653, and 0.8443, respectively. Compared with other methods, our method has the advantages of strong anti-noise ability, high detection accuracy and fast detection speed. In addition, we propose a method for calculating the physical size of cracks. Through error analysis, the relative errors of calculating the length and width of the cracks are 0.056 and 0.084 respectively, which can meet the needs of engineering inspection.