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 B\'ezier 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.

本研究解决了在雾霾和低光等恶劣天气条件下对象检测性能不佳的问题。提出了一种图像自适应目标检测方法，引入贝塞尔曲线像素级过滤器和局部核过滤器，以统一经典图像处理功能并提升检测性能。实验结果表明，ERUP-YOLO在多种恶劣天气条件下实现了优越的检测表现。

ERUP-YOLO：通过统一图像自适应处理增强恶劣天气下的目标检测鲁棒性