HV-Net: Coarse-to-Fine Feature Guidance for Object Detection in Rainy Weather

Object detection algorithms have been extensively researched in the field of computer vision, but they are still far from being perfect, especially in adverse weather conditions such as rainy weather. Traditional object detection models suffer from an inherent limitation when extracting features in adverse weather due to domain shift and weather noise, leading to feature contamination and a significant drop in model performance. In this paper, we propose a novel staged detection paradigm inspired by the human visual system, called Human Vision Network (HV-Net). HV-Net first extracts coarse-grained edge features and leverages their insensitivity to weather noise to reduce feature contamination and outline the edges of large and medium-sized objects. The subsequent network uses deep fine-grained features and edge-attentional features to generate clear images, enhancing the understanding of small objects that might be missed in edge detection and mitigating weather noise. The staged end-to-end pipeline design allows the clear features to be shared throughout the network. We validate the proposed method for rainy weather object detection on both real-world and synthetic datasets. Experimental results demonstrate significant improvements of our HV-Net compared to baselines and other object detection algorithms.