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Title: 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.  more » « less
Award ID(s):
2152258
PAR ID:
10584682
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3315-0592-9
Page Range / eLocation ID:
1418 to 1425
Format(s):
Medium: X
Location:
Edmonton, AB, Canada
Sponsoring Org:
National Science Foundation
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