Abstract
Rivers and canals flowing through cities are often used illegally for dumping trash that contaminates freshwater channels, causes blockage in sewerage leading to urban flooding. The dumped trash is often found floating on the water surface. We propose to automatically identify this trash through visual inspection with the eventual goal of quantification, an early warning system to avoid blockages and urban flooding. The trash could be disfigured, partially submerged, or clumped together with other objects which obscure its shape and appearance. Thus, we consider surface trash as a blob detection problem that could either be solved as object detection or image segmentation or both. To this extent, we evaluate and compare several deep-learning-based object detection and segmentation algorithms. Unlike ocean trash, to the best of our knowledge, there is no large dataset on urban trash on water channels. Thus, using IoT-based camera nodes at multiple water channels, we collected a large dataset containing 48, 450 trash objects annotated for both bounding box and segmentation (the dataset will be made publicly available (Dataset is available at https://cvlab.lums.edu.pk/watertrash/)). In addition, we also propose modifications in state-of-the-art detection and segmentation algorithms to cater to an issue such as partially submerged, varying object sizes, and edge-based computing.
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Tharani, M., Amin, A.W., Rasool, F., Maaz, M., Taj, M., Muhammad, A. (2021). Trash Detection on Water Channels. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_31
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