KD-DETR: multi-scale lightweight DETR based on knowledge distillation for road crack detection

Road crack detection is a critical task in infrastructure health monitoring. Deep learning models deployed on edge devices, however, often suffer from information loss during feature extraction and high computational costs, limiting their practicality in real-world engineering applications. To address these challenges, we propose KD-DETR, a lightweight crack detection framework based on the DETR object detector. Its structural optimizations include an Inverted Residual Module with Cross-Global Attention (IRMB_CGA) to efficiently capture long-range dependencies for cracks in complex road backgrounds, a FreqFusion-BiFPN module that enhances crack edge responses via adaptive frequency-domain filtering and improves multi-scale feature fusion through dynamic recalibration, and a model lightweighting strategy that uses channel attention-guided feature distillation to achieve both model compression and effective domain-specific knowledge transfer.Compared to the original detector RT-DETR, our KD-DETR reduces the number of parameters by 6.36% and computational FLOPs by 18.5%, effectively balancing detection precision and efficiency. Experiments on road defect datasets demonstrate that KD-DETR achieves superior mIoU, lower parameter count and FLOPs, mAP50 of 78.3% and 75.8%, and higher recall for small-scale cracks. Overall, it significantly reduces computational costs while boosting accuracy and recall, enabling efficient, high-precision road defect detection on resource-constrained edge devices.