RAF-RCNN: Adaptive Feature Transfer from Clear to Rainy Conditions for Improved Object Detection

In the challenging realm of object detection under rainy conditions, visual distortions significantly hinder accuracy. This paper introduces Rain-Adapt Faster RCNN (RAF-RCNN), an innovative end-to-end approach that merges advanced deraining techniques with robust object detection. Our method integrates rain removal and object detection into a single process, using a novel feature transfer learning approach for enhanced robustness. By employing the Extended Area Structural Discrepancy Loss (EASDL), RAF-RCNN enhances feature map evaluation, leading to significant performance improvements. In quantitative testing of the Rainy KITTI dataset, RAF-RCNN achieves a mean Average Precision (mAP) of 51.4% at IOU [0.5, 0.95], exceeding previous methods by at least 5.5%. These results demonstrate RAF-RCNN's potential to significantly enhance perception systems in intelligent transportation, promising substantial improvements in reliability and safety for autonomous vehicles operating in varied weather conditions.