Cross-Domain Object Detection for Intelligent Driving Based on Joint Distribution Matching of Features and Labels

Current cross-domain adaptive object detection methods primarily focus on reducing domain shift by learning domain-invariant features through feature distribution alignment. However，they often overlook label distribution shift issues caused by variations in object combinations and class imbalances in real-world scenarios，resulting in poor generalization performance. To address this，this paper proposes a novel domain adaptive object detection algorithm that simultaneously aligns domain distributions at both feature and label levels. Firstly，an image-level classification embedding module is introduced to enhance the transferability and discriminability of global features through contrastive learning. Next，a class-level distribution alignment module is presented to achieve inter-domain multimodal structure alignment through multi-level feature alignment. Finally，an enhanced consistency regularization module is proposed to achieve cross-domain label distribution alignment through region-based consistency regularization. Experimental results across multiple datasets demonstrate that the proposed domain alignment algorithm effectively improves semantic consistency both before and after cross-domain data adaptation. This provides a valuable solution for the effective deployment of autonomous vehicles in cross-domain scenarios. © 2023 SAE-China. All rights reserved.