提出轻量级去雨网络，提升雨夜车辆检测精度。
ProvRain: Rain-Adaptive Denoising and Vehicle Detection via MobileNet-UNet and Faster R-CNN
- 用MobileNet-UNet+课程学习法自适应去雨
- 雨夜检测准确率提升8.94%，召回率升10.25%
- 适合车载视觉系统与夜间行车安全研究
夜间行车中，提前检测来车对安全至关重要。然而雨雪等天气及相机噪声严重影响检测效果。本文提出ProvRain框架，采用轻量级MobileNet-UNet结构，并通过课程学习在合成数据与真实PVDN数据混合训练下，增强模型对恶劣天气的泛化能力。该框架与基线Faster R-CNN在PVDN数据集上对比，雨夜条件下车辆检测准确率提升8.94%，召回率提高10.25%。同时，所提MobileNet-UNet在去噪方面表现优异：PSNR提升10-15%，SSIM增长5-6%，感知误差（LPIPS）降低最高达67%，优于现有基于Transformer的方法。
原文摘要 · Abstract (English)
Provident vehicle detection has a lot of scope in the detection of vehicle during night time. The extraction of features other than the headlamps of vehicles allows us to detect oncoming vehicles before they appear directly on the camera. However, it faces multiple issues especially in the field of night vision, where a lot of noise caused due to weather conditions such as rain or snow as well as camera conditions. This paper focuses on creating a pipeline aimed at dealing with such noise while at the same time maintaining the accuracy of provident vehicular detection. The pipeline in this paper, ProvRain, uses a lightweight MobileNet-U-Net architecture tuned to generalize to robust weather conditions by using the concept of curricula training. A mix of synthetic as well as available data from the PVDN dataset is used for this. This pipeline is compared to the base Faster RCNN architecture trained on the PVDN dataset to see how much the addition of a denoising architecture helps increase the detection model's performance in rainy conditions. The system boasts an 8.94\% increase in accuracy and a 10.25\% increase in recall in the detection of vehicles in rainy night time frames. Similarly, the custom MobileNet-U-Net architecture that was trained also shows a 10-15\% improvement in PSNR, a 5-6\% increase in SSIM, and upto a 67\% reduction in perceptual error (LPIPS) compared to other transformer approaches.
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