Domain Adaptive Object Detection with Dehazing Module

Gang Pan, Kang Liu, Jingxin Li, Rufei Zhang, Sheng Shen, Zhiliang Zeng, Jiahao Wang, Di Sun

Abstract: In foggy object detection tasks, the presence of airborne particles reduces imaging clarity, resulting in a significant decrease in detection accuracy. Existing fog removal networks lack evaluation metrics for high-level tasks, and connecting the fog removal network limits the adaptability of the object detection network. To address these issues, this paper proposes training the fog removal network with a perceptual-loss approach involving the object detection network. This approach aims to enhance the accuracy of the fog removal network in advanced tasks and overcome the constraints of quantitative evaluation indexes like PSNR. We compare the results of training DefogNet with perceptual loss and pixel-level loss, and obtain the best results in terms of PSNR and SSM indices using both losses. Although the object detection network connected to the dehazing network can handle detection task in foggy scenes, its accuracy decreases in such scenarios. For this reason, we propose the DefogDA-FasterRCNN network, which incorporates domain adaptation into the integrated network, and makes the object detection module domain-adaptive for both foggy and non-foggy domains that pass through the dehazing module. Foggy images will obtain clearer features through the fog removal network and the negative impact of foggy images through the fog removal network will be weakened by the domain adaptation.