Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
This work addresses a critical safety issue for autonomous vehicles by enhancing object detection performance in adverse weather, though it is incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of object detection for autonomous driving in foggy weather by proposing a domain adaptive framework that reduces the domain gap between clear and foggy conditions, achieving improved accuracy on public benchmarks.
Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers differ significantly. The object detection model trained under clear weather might not be effective enough in foggy weather because of the domain gap. This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather. Our method leverages both image-level and object-level adaptation to diminish the domain discrepancy in image style and object appearance. To further enhance the model's capabilities under challenging samples, we also come up with a new adversarial gradient reversal layer to perform adversarial mining for the hard examples together with domain adaptation. Moreover, we propose to generate an auxiliary domain by data augmentation to enforce a new domain-level metric regularization. Experimental results on public benchmarks show the effectiveness and accuracy of the proposed method. The code is available at https://github.com/jinlong17/DA-Detect.