能见度
杠杆(统计)
计算机科学
计算机视觉
对象(语法)
图像复原
气象学
薄雾
编码器
目标检测
图像(数学)
图像处理
人工智能
物理
模式识别(心理学)
光学
操作系统
作者
Yizhan Li,Rongwei Yu,Junjie Shi,Lina Wang
标识
DOI:10.1109/icassp48485.2024.10446872
摘要
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.
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