HLA-HOD: Joint High-Low Adaptation for Object Detection in Hazy Weather Conditions

Abstract

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low-level adaptation methods that combine one detector with an additional dehazing network and (ii) high-level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question - if combining both low-level adaptation and high-level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High-Low Adaptation Object Detection paradigm (HLA-HOD) in hazy weather conditions. By combining both low-level adaptation and high-level adaptation, HLA-HOD achieves superior performance on hazy images without requiring ground-truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state-of-the-art low-level and high-level adaptation methods by a large margin both quantitatively and qualitatively.