Road crack detection network under noise based on feature pyramid structure with feature enhancement (road crack detection under noise)

Abstract

Road crack detection is an important task for road safety and road maintenance. In the past, people made use of manual detection methods and tried to use computer vision to detect crack. The most prominent feature in recent years is the use of deep learning. However, there is no good deep learning method for road crack detection under noise. This challenge is faced bravely. First, a noise crack dataset is proposed, consisting of multiple noise crack images which is called NCD. Then, an adaptive bilateral filtering algorithm is developed, which can reduce the influence of noise. Finally, a new crack detection network with two new modules is designed. In the end, it is found that all the parts have promoting effects on crack detection under noise. Compared with other state-of-the-art methods, this method performs better, especially in road crack detection under noise. When evaluating the well-known crack500 test set, ODS F-measure of 0.628 is achieved. Besides, this method is also evaluated in another five datasets. Significantly, ODS F-measure of 0.545 is achieved, 4.0% higher than state-of-the-art on GAPs384.