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

• URL: http://arxiv.org/abs/2307.09676v3
• Date: Tue, 25 Jun 2024 02:16:41 GMT
• Title: Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
• Authors: Jinlong Li, Runsheng Xu, Xinyu Liu, Jin Ma, Baolu Li, Qin Zou, Jiaqi Ma, Hongkai Yu,
• Abstract summary: Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. 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.
• Score: 44.711384869027775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.

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