Abstract: Road scene object detection is an important part of the intelligent transport field, which is directly related to the implementation of many intelligent transport applicative technologies. However, the existing domain generalization techniques for road scene object detection generally have the problems of insufficient domain-invariant feature extraction, low detection accuracy, and weak generalization ability. To address this problem, this paper proposed a domain generalization method for road scene object detection under complex weather conditions. The paper designed a road scene domain-invariant feature generation model, extracted the intra-domain invariant features and inter-domain invariant features of the source domain images, respectively, and generated more diverse road scene domain-invariant features under complex weather conditions to improve the generalization ability of the object detection model. On this basis, a domain generalization model of road scene object detection was designed, and the self-distillation mechanism was introduced to make the features extracted by the object detection model have rich domain-invariant features to further enhance its generalization ability, so as to improve the detection accuracy of the object detection model. The experimental results show that the performance of the object detection domain generalization model in this paper is significantly improved compared with the comparison models. The model can significantly improve the generalization ability and detection accuracy of the object detection model. Among them, the F1-score is increased by 0.042-0.051 compared with the baseline object detection model, and the mean average precision (mAP) is increased by 3.0%-5.9%, which proves the effectiveness and superiority of the object detection domain generalization method proposed in this paper.

Key words: road scene, object detection, domain generalization, domain-invariant feature generation, self-distillation