Abstract
Single image deraining has made impressive progress in recent years. However, the proposed methods are heavily based on high-quality synthetic data for supervised learning which are not representative of practical applications with low-quality real-world images. In a real setting, the rainy images portray a scene with a complex degradation caused by the rain weather and the low-quality factors. The goal of this paper is to investigate the impact of two visual factors that affect vision tasks: image quality and rain effect. To evaluate this, an image dataset with images varying these factors has been created. Aiming to evaluate them, different object detection algorithms are applied and evaluated on the dataset. Our findings indicate that the fine-tuned models can efficiently cope with this problem regardless of the rain intensity of the scene, however it is greatly affected by the image quality gap.
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Acknowledgements
This work was supported by FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo (grants #15/22308-2, #2019/01077-3). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Araujo, I.B., Tokuda, E.K., Cesar, R.M. (2020). The Impact of Real Rain in a Vision Task. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_21
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