Abstract
The monitoring of illegal dumping of urban solid waste requires timeliness. Conventional methods are costly and have difficulty resolving the problem in remote regions timely. In this paper, a method based on semantic segmentation technology, which considers the multiscale characteristics and the disordered distribution of garbage, is proposed for automatic detection of surface solid waste in unmanned aerial vehicle (UAV) images. First, an asymmetrically decomposed atrous convolutional group was embedded to the center of the semantic segmentation network and the residual structure is used to enhance the learning ability of the spatial relationships. Then, a semantic flow alignment module was combined with attention mechanism to solve the disordered edges of information fusion. Experimental results demonstrate that the proposed method achieved better segmentation performance.
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Acknowledgements
This work was supported by Science and Technology Program of Zhejiang Province No. 2022C35070 and the Postgraduate Research and Practice Innovation Program of Jiangsu Province No. KYCX21_1349.
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Liu, Y., Gou, P., Nie, W., Xu, N., Zhou, T., Zheng, Y. (2023). Urban Surface Solid Waste Detection Based on UAV Images. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 8th China High Resolution Earth Observation Conference (CHREOC 2022). CHREOC 2022. Lecture Notes in Electrical Engineering, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-19-8202-6_12
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DOI: https://doi.org/10.1007/978-981-19-8202-6_12
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