Beyond the Snowfall: Enhancing Snowy Day Object Detection Through Progressive Restoration and Multi-Feature Fusion

In the field of computer vision, object detection is a prominent and challenging task. Despite the favorable performance of deep learning-based object detection techniques on clear images, it fails in inclement weather conditions like snow because of image degradation. Recent efforts have explored using image restoration methods to enhance degraded images before object detection. However, direct restoration can sometimes cause new disturbances, impeding detection performance improvements. To address this issue, we propose a joint framework that connects the iterative desnow module and detection module in an end-to-end manner. Specially, we design an Advantage Union structure for multi-feature fusion, which effectively combines original, intermediate, and restored features, reducing potential information loss from restoration. Experimental results show that our method achieves higher accuracy compared to the recent state-of-the-art methods in both synthetic dataset and real-to-world snowy images.