Multi-Level Guided Discrepancy Learning for Source-Free Object Detection in Hazy Conditions

Haze deteriorates the quality of captured images, which severely limits the accuracy of clean image-trained object detectors in hazy conditions. Source-free domain adaptation (SFDA) aims to adapt a clean source image-trained detector to the unlabeled hazy target domain without access to the clean source domain data. However, existing source-free object detection (SFOD) methods encounter two issues when leveraging pseudo labeling paradigm: 1) the large domain shift between clear and hazy images may introduce noises in pseudo labels, 2) the single confidence threshold-based methods may ignore valuable information of the low-confidence samples. To address these issues, we propose a multi-level guided discrepancy learning-based approach for SFOD in hazy conditions, named MGDL. Specifically, we first propose a differentiated enhancement module (DEM) to intentionally augment the diversity of data styles. It uses dehazing and random perturbation to generate reliable high-quality labels and strengthen the tolerance to haze-related factors. To enhance the consistency constraints for discrepancy learning, we propose a multi-level guiding strategy (MLGS) which consists of a dual label-level and an instance-level guidance. Considering that false negatives may dominate in noisy labels, we propose a dual label-level guiding (DLG) strategy to excavate comprehensive useful information from high-and low-confidence samples. Besides, an instance-level contrastive learning (ICL) approach is proposed to guide the model to focus on objects and make the model insensitive to style at the same time. Extensive experiments conducted on multiple datasets have demonstrated that our method achieves superior performance over the state-of-the-art SFOD methods.