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| Tailings Pond Dry Beach Length Measurement Based on Deep Learning Water Instance Segmentation |
| SUN Yeqing1,CHEN Hongfei2,TONG Renyuan1 |
1. China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Institute of Hydraulics and Estuary, Hangzhou, Zhejiang 310020, China |
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Abstract A method based on YOLOv8 for water instance segmentation has been proposed, achieving rapid, efficient, and accurate measurement of the dry beach length of tailings ponds under real-time video streams. Firstly, a high-quality water instance segmentation COCO dataset is completed. Secondly, mainstream deep learning instance segmentation algorithms are analyzed, and the YOLOv8 model is chosen to efficiently recognize the waterline and output image coordinates. Finally, the internal and external parameters of the camera are calibrated. By applying the principles of camera imaging and installing surveillance cameras at the end of the tailings pond, the dry beach length is predicted. Experiments prove that this model can not only accurately predict the dry beach length but also has good stability in segmenting the water boundaries of different tailings ponds. It has a good effect on non-contact measurement in the field under real-time video stream mode, with an error controlled within 2%.
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Received: 24 September 2023
Published: 29 November 2024
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2024, Vol. 45 
