Topology-Aware Lightweight Crack Segmentation and Automated Quantification via Synergistic Spatial-Channel Enhancement

Abstract

Pavement crack detection is a critical prerequisite for road maintenance. However, traditional manual inspection and existing deep learning methods struggle to balance inference efficiency with the segmentation accuracy of fine-grained cracks, often leading to topological fractures in slender cracks and missed detections in low-contrast environments. To address these challenges, this paper proposes CBAM-LKC-YOLOv8-seg, a lightweight end-to-end framework designed for high-precision segmentation and automated quantification. The core innovations include (1) a synergistic feature enhancement mechanism that integrates a lightweight convolutional block attention module with 7 × 7 large kernel convolution. This design effectively recalibrates feature weights to suppress background noise while expanding the effective receptive field to preserve the topological continuity of slender cracks. (2) An adaptive training strategy tailored for extreme class imbalance, utilizing optimized loss weights to enhance boundary sensitivity. (3) A robust end-to-end quantification pipeline that fuses morphological processing with skeletonization algorithms to translate segmentation masks into precise geometric parameters (length and width). Experiments on the CRACK500 dataset and cross-scenario test sets demonstrate that the proposed model achieves an mIoU of 77.0% (8.7% higher than the native YOLOv8-seg) and a recall rate of 82.5% for small cracks ( < 5    pixels ). Furthermore, the framework controls measurement errors within 3% while maintaining a lightweight footprint of 13.1M parameters and an inference speed of 28.9 FPS. This method effectively resolves the trade-off between accuracy and efficiency, providing a reliable automated tool for large-scale road maintenance.