Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, this such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detection in foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and object-level are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios. The code is available at https://github.com/jinlong17/DA-Detect.


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    Title :

    Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather


    Contributors:
    Li, Jinlong (author) / Xu, Runsheng (author) / Liu, Xinyu (author) / Ma, Jin (author) / Li, Baolu (author) / Zou, Qin (author) / Ma, Jiaqi (author) / Yu, Hongkai (author)

    Published in:

    Publication date :

    2025-02-01


    Size :

    4258559 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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