Degradation Type-Aware Image Restoration for Effective Object Detection in Adverse Weather

作者：Xiaochen Huang, Xiaofeng Wang, Qizhi Teng, Xiaohai He, Honggang Chen · 发表于：Sensors · 年份：2024 · DOI：10.3390/s24196330 · 被引用次数：13 · 研究领域：Image Enhancement Techniques、Advanced Neural Network Applications、Advanced Image Fusion Techniques

Despite significant advancements in CNN-based object detection technology, adverse weather conditions can disrupt imaging sensors' ability to capture clear images, thereby adversely impacting detection accuracy. Mainstream algorithms for adverse weather object detection enhance detection performance through image restoration methods. Nevertheless, the majority of these approaches are designed for a specific degradation scenario, making it difficult to adapt to diverse weather conditions. To cope with this issue, we put forward a degradation type-aware restoration-assisted object detection network, dubbed DTRDNet. It contains an object detection network with a shared feature encoder (SFE) and object detection decoder, a degradation discrimination image restoration decoder (DDIR), and a degradation category predictor (DCP). In the training phase, we jointly optimize the whole framework on a mixed weather dataset, including degraded images and clean images. Specifically, the degradation type information is incorporated in our DDIR to avoid the interaction between clean images and the restoration module. Furthermore, the DCP makes the SFE possess degradation category awareness ability, enhancing the detector's adaptability to diverse weather conditions and enabling it to furnish requisite environmental information as required. Both the DCP and the DDIR can be removed according to requirement in the inference stage to retain the real-time performance of the detection algorithm. Ex...