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

TL;DR

This paper introduces a domain-adaptive object detection framework for autonomous driving that effectively handles foggy and rainy weather by minimizing domain gaps at image and object levels, using adversarial techniques and data augmentation.

Contribution

It proposes a novel adversarial gradient reversal layer and auxiliary domain generation methods to improve detection in adverse weather conditions, addressing limitations of traditional supervised models.

Findings

Significant improvement in detection accuracy in foggy and rainy scenarios

Effective domain gap minimization at image and object levels

Enhanced model robustness through adversarial mining and data augmentation

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 benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.