Diff-HOD: Diffusion Model for Object Detection in Hazy Weather Conditions.

The presence of haze negatively affects the visibility of captured images, posing challenges for general object detection models. We observe that current techniques exhibit three limitations: 1) they typically view image restoration and object detection as separate tasks; 2) they disregard potential details in degraded images that benefit detection; and 3) they lack sufficient recognition ability under haze interference. To this end, we propose a novel Diffusion Model (Diff-HOD) for Object Detection in Hazy weather conditions. Diff-HOD is a multi-task joint learning paradigm that integrates low-level image restoration and high-level object detection. Specifically, to bridge restoration and detection, we present a lightweight restoration module that mitigates the impact of weather-specific information, guiding the shared image encoder to provide high-quality features. We further leverage the excellent modeling ability of diffusion models to enhance the detection capability in hazy conditions. Moreover, we introduce an IoU-aware attention module that utilizes IoU as spatial priors to strengthen relevant features. Extensive experiments demonstrate that our Diff-HOD performs favorably against representative state-of-the-art approaches on both synthetic and natural datasets.