Adaptive Canny and Semantic Segmentation Networks Based on Feature Fusion for Road Crack Detection

Road cracks, which are a common hazard in pavements throughout the life cycle of a road, can degrade the performance of the road, shorten its service life, and endanger the safety of vehicles. Traditional vision machine detection methods can detect road crack details but suffer from poor stability and generalization ability, whereas semantic segmentation detection, although more stable, cannot track fine road crack information. To combine the advantages of both methods and improve the accuracy of road crack detection, a novel feature fusion road crack detection method is proposed in this study. First, the bilateral filter and four-way Sobel operator are introduced into the Canny algorithm to enhance the noise reduction effect and extract edge features more effectively. Second, the dynamic threshold is generated adaptively by the gradient information after non-maximum suppression. Subsequently, the detection map is morphologically processed, the connected areas are ranked, and the bilateral filter parameters are adjusted based on the detection results. The Canny road crack detection map is then extracted by the convolutional feature extraction module, fused with the low feature layer in the DeepLabV3+ detection network, and finally stitched with the high feature layer; the resulting map is obtained after convolutional feature extraction. The method was validated on the publicly available complex road crack dataset CRACK500; the experimental results showed that feature fusion outperformed the adaptive Canny, DeepLabV3+, Unet, PSPnet, and ICnet algorithms by more than 6.5% on the Mean Intersection over Union(MIoU) and also in Mean Absolute Error(MAE) by effectively combining crack features and improving the detection accuracy.