Abstract
It is important to observe and split water region to help acquire the water quality and supervise water environment. Water segmentation is a task to separate water region from images. Due to the specular nature of the water surface, various types of reflections usually appear on the water surface, which can change significantly with weather and lighting changes, it is difficult for general segmentation to work. According to the characteristics of waters, i.e. wide area and reflection, we propose a asymmetric interaction module (AIM) converge the features to a larger receptive field. Further, with this powerful module, we design the asymmetric multiscale interaction network, which can maintain the features of each scale and reassign the weights of features at different scales. We conduct extensive experiments on Hubei water dataset we constructed, The results show the framework effectively improves the accuracy of water segmentation and greatly improves the visual effect of segmentation, which is 5.9% higher in self-made dataset with advanced methods.
This work was supported by the Science and technology project innovation fund of Hubei Three Gorges Laboratory under Grant SC215002, National Natural Science Foundation of China under Grant 62072350, Grant 62171328; and the Hubei Technology Innovation Project under Grant 2019AAA045.
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Chen, J., Lu, T., Zhang, Y., Fang, W., Rao, X., Zhao, M. (2023). Water Segmentation via Asymmetric Multiscale Interaction Network. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_16
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DOI: https://doi.org/10.1007/978-981-99-0856-1_16
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