Progressive Enhancement Dehazing for object detection in extreme weather

Existing object detection technologies have made significant advances in perception systems for autonomous driving. However, accurately detecting objects in extreme weather conditions, especially fog, remains a significant challenge. On the one hand, current methods struggle to balance image enhancement and object detection, leading to the neglect of essential information that could improve detection accuracy. On the other hand, the lack of mature datasets in this area limits many studies to synthetic fog data generated from the prior-based atmospheric scattering model, which inevitably restricts further performance improvements. To address these challenges, we propose the Progressive Enhancement Dehazing You Only Look Once (PED-YOLO) method, which processes images in real time under foggy conditions to improve object detection. We design a novel progressive image processing module that follows a unique paradigm of progressive supervised learning, which gradually processes the image from small size to large size, to effectively overcome the challenge of processing large-sized images at once. Moreover, we develop a small convolutional network module that focuses on each channel of the image and enables more accurate adaptive prediction of the parameters of the filters. In addition, we develop a novel style transfer model to generate simulated fog images that more closely resemble real fog images and train them together to bring the synthetic fog domain closer to the real fog domain and improve the generalizability. We evaluate our method extensively on several popular datasets, and the experimental results show the superior performance of PED-YOLO, highlighting its potential to advance autonomous driving.