LYEU-Net: A lightweight yet efficient U-Net with multibranch feature aggregation for pavement crack segmentation

Abstract

Accurate segmentation of pavement cracks is crucial for intelligent transportation systems. However, a critical research gap exists in current methodologies: high-performance models require substantial computational resources, which renders them unsuitable for edge deployment, whereas existing lightweight models often sacrifice segmentation accuracy for low resource use. To address this challenge, we propose LYEU-Net, a lightweight yet efficient U-Net architecture that is designed to balance accuracy and efficiency. The model incorporates three key innovations: a feature extraction module (FEM) for improved salient feature extraction using skip connections, a multibranch feature extraction module (MBM) for efficient multiscale contextual feature capture, and a feature aggregation module (FAM) for adaptive feature weighting to refine decoder outputs. Experimental results across four diverse datasets (Crack500, ShadowCrack, GAPs384, and AigleRN-TRIMM) show that LYEU-Net achieves the best mIoU among the compared methods with only 0.134M parameters and 0.347G FLOPs. It improves precision and mIoU over existing methods, although slight decreases in recall or F1 score on some datasets indicate a precision–recall trade-off. This work provides a viable solution for real-time crack detection on mobile devices. The code and data are available at https://github.com/yangbing668/LYEU-Net.