Domain-Invariant Feature Enhancement Domain Adaptation for Cross-Scene Road Damage Detection

Due to the existence of domain shift, the detection accuracy of road damage detection will drop significantly when the training images and test images are from different scenes. To address this problem, we propose a domain-invariant feature enhancement domain adaptation method (DIFEDA) based on the You Only Look Once (YOLO) series object detectors. We integrate three domain-invariant feature decoupling (DIFD) modules on the backbone to decouple multi-scale domain-invariant features through two-stage adversarial learning. The decoupled features are fed back to the backbone to realize domain-invariant feature enhancement. We construct a spatial and frequency domain perception (SPFD) module in the DIFD to decouple local and global domain-invariant features from the spatial level and frequency level, respectively. We also design a region segmentation decoder (RSD) to make the DIFD pay more attention to the domain-invariant feature extraction in the damaged region, thereby suppressing the interference of background information. We apply DIFEDA to YOLOv8, YOLOv9, YOLO11, and YOLOv12, and conduct extensive experiments in three cross-scenes. The experimental results show that DIFEDA can significantly improve the performance of all baselines, with up to 8.5% and 9.8% improvement in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i>@0.5 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i>1, respectively, proving the effectiveness and generalization of our method.