RDFNet: Real-time Object Detection Framework for Foggy Scenes

Tianle Fang, Zhenbing Liu, Yutao Tang, Yingxin Huang, Haoxiang Lu, Chuangtao Zheng

Abstract: Detecting objects in foggy scenes remains a persistent challenge, as detectors trained for fair weather often struggle with foggy data due to blurring effects. Previous methods based on domain adaptation, multi-task learning, etc. try to tackle this challenge, but they often fail to achieve an optimal balance between model complexity and accuracy. Hence, we propose a multi-branch pooling information fusion (MPIF) module, which combines local and global information to enhance feature representation with minimal computational overhead. We also design a lightweight multi-scale dehazing network (LMDNet) and utilize the multi-task learning strategy to adaptively incorporate dehazing feature information into the object detection network. Leveraging these core modules with additional design optimizations, we construct a novel real-time object detection framework, called RDFNet, for foggy images. Extensive experiments demonstrate that RDFNet outperforms SOTA detection methods for foggy scenes while enjoying less complexity and faster detection speeds. The source code will be released at https://github.com/PolarisFTL/RDFNet.

External IDs:dblp:conf/icmcs/FangLTHLZ25