CrackLite-Net: A Sustainable Transportation-Oriented Real-Time Lightweight Network for Adaptive Road Crack Detection

Accurate and timely detection of road surface cracks plays a crucial role in ensuring sustainable infrastructure maintenance and improving road safety, particularly under complex and dynamic environmental conditions. However, existing deep learning-based detection methods often suffer from high computational overhead, limited scalability across diverse crack patterns, and insufficient robustness against complex background interference, hindering real-world deployment in resource-constrained UAV platforms. To address these challenges, this study proposes CrackLite-Net, an improved and lightweight variant of the YOLO12n architecture tailored for adaptive UAV-based road crack detection. First, a novel GhostPercepC2f backbone module is introduced, combining ghost feature generation with axis-aware attention to enhance spatial perception of crack structures while significantly reducing redundant computations and model parameters. Second, a Spatial Attention-Enhanced Feature Pyramid Network (SAFPN) is developed to perform adaptive multi-scale feature integration. By incorporating spatial attention and energy-guided filtering, SAFPN strengthens the representation of cracks with varying widths, orientations, and shapes. Third, the Selective Channel-Enhanced Cross-Stage Fusion module (SC2f) consolidates channel-wise feature dependencies using an adaptive lightweight convolution mechanism, effectively suppressing noise and improving feature discrimination in visually cluttered road scenes. Experimental evaluations on the newly constructed LCrack dataset demonstrate that CrackLite-Net achieves a mAP of 92.3% with only 2.2 M parameters, outperforming YOLO12 by 3.9% while delivering superior efficiency. Cross-dataset validation on RDD2022 further confirms the model’s strong generalization capability across different environments and imaging conditions. Overall, the results highlight CrackLite-Net as an effective, energy-efficient, and deployable solution for sustainable road infrastructure inspection using UAV platforms.