Boundary-Guided Real-Time Semantic Segmentation and Pixel-Level Quantification of Pavement Cracks

Timely and accurately extracting and assessing pavement cracks is crucial for intelligent transportation systems (ITS) to improve road maintenance and safety. In this paper, we present an automated framework for crack semantic segmentation and quantification using optical images. First, a unique boundary-guided real-time high-resolution network is proposed, termed as BulletNet, for crack semantic segmentation. BulletNet is a bullet-head structure that can retain crack details while ensuring real-time inference speed, in which a Cross-Scale Global Attention (CSGA) module is designed to enhance global feature representation and pixel-level relations, as well as a Boundary-Guided Fusion (BGF) module proposed to utilize boundary features to guide the fusion of crack details and contextual information. Second, a Pixel-level Crack Quantification (PCQ) algorithm is proposed for complex cracks, incorporating an Improved Discrete Skeleton Evolution (IDSE) method to optimize skeleton pruning for accurate crack length and a normal vector correction method to adjust propagation direction for precise crack width. Comprehensive experiments on three datasets showed that the proposed BulletNet surpassed the comparative models in terms of efficiency and performance, with average F1-score, mIoU, and Frames per second (FPS) of 87.20%, 88.70%, and 125.53, respectively. In addition, tested on 200 images, the PCQ calculated the crack maximum widths and lengths with an average relative error of 6.96% and 4.62%, respectively. Finally, BulletNet was deployed on edge devices for field testing, and a system based on the PCQ algorithm was developed to validate the effectiveness of the entire framework.