Semantic Segmentation on Raindrop Degraded Images Using Two-Stage Dual Teacher-Student Learning

Existing semantic segmentation methods face challenges when processing input images degraded by raindrops on the lens or windshield. Unlike other adverse conditions such as fog and nighttime, which degrade visual quality, raindrops not only impair visual appearances but also introduce misleading occlusion, leading to significant performance drops in current models. The novelty of our approach lies in our twostage, dual teacher-student framework. We tackle the complex problem of raindrop degradation by dividing it into two distinct challenges: degraded visual appearance and raindrop occlusion. These challenges are then addressed individually in two stages, utilizing two pairs of teacher-student networks. This division enables the networks to develop specialized expertise in handling each aspect of raindrop degradation, enabling their collaboration to achieve superior performance. In the first stage, one teacher-student pair focuses on learning to extract information from visual degraded areas. Building on this, the second teacher-student pair focuses specially on the raindrop occlusion. As such, unlike the existing methods, our approach employs a collaborative approach to decompose and address raindrop-induced degradations. In the second stage, we introduce a mask-based recovery technique to identify and rectify areas that likely contain misleading information, thus further refining the predictions. Additionally, this stage encourages both pairs to expand knowledge by swapping their specialized expertise. Our method achieves a performance of 60.3 mIoU on Rainy WCity (Zhong et al. 2022) and 72.8 mIoU on ACDC Rainy (Sakaridis, Dai, and Van Gool 2021), representing an improvement of +4.4 mIoU and +2.3 mIoU over the existing state-of-the-art methods, respectively.