ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing

Yuka Ogino, Yuho Shoji, Takahiro Toizumi, Atsushi Ito; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8586-8594

Abstract


We propose an image-adaptive object detection method for adverse weather conditions such as fog and low-light. Our framework employs differentiable preprocessing filters to perform image enhancement suitable for later-stage object detections. Our framework introduces two differentiable filters: a Bezier curve-based pixel-wise (BPW) filter and a kernel-based local (KBL) filter. These filters unify the functions of classical image processing filters and improve performance of object detection. We also propose a domain-agnostic data augmentation strategy using the BPW filter. Our method does not require data-specific customization of the filter combinations parameter ranges and data augmentation. We evaluate our proposed approach called Enhanced Robustness by Unified Image Processing (ERUP)-YOLO by applying it to the YOLOv3 detector. Experiments on adverse weather datasets demonstrate that our proposed filters match or exceed the expressiveness of conventional methods and our ERUP-YOLO achieved superior performance in a wide range of adverse weather conditions including fog and low-light conditions.

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[bibtex]
@InProceedings{Ogino_2025_WACV, author = {Ogino, Yuka and Shoji, Yuho and Toizumi, Takahiro and Ito, Atsushi}, title = {ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {8586-8594} }