Crack segmentation by severity in 3D asphalt pavement images using deep learning models

In recent years, Convolutional Neural Networks (CNNs) have successfully automated pavement crack segmentation, outperforming traditional methods. Although researchers have proposed multiple models for binary segmentation, their performance in segmenting cracks according to their severity (multiclass segmentation) has not yet been tested, despite the vital importance of crack severity identification for pavement maintenance operations. Therefore, this study evaluates the performance of three CNN models from the crack segmentation literature (U-Net B, CrackNet II, and CrackNet V), and several state-of-the-art semantic segmentation models (CCNet, DANet, Segformer, and OCRNet), for binary and multiclass segmentation. All models are trained and evaluated for both binary and multiclass segmentation using 3D asphalt pavement images collected from different highways in Chile. For binary segmentation, U-Net B achieved the highest performance with an F1-score of 0.76, correctly identifying most crack pixels. For multiclass segmentation, OCRNet achieved the highest F1 score of 0.58. Despite the relatively low F1-score in multiclass segmentation, the results demonstrate that CNN models can identify multiple severity levels on cracks based on their local width. However, they are unable to assign a unique severity to cracks that exhibit varying widths along their length, which reduces their F1-score.