{"id":11701,"date":"2026-09-11T17:16:19","date_gmt":"2026-09-11T09:16:19","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11701"},"modified":"2026-09-11T20:03:06","modified_gmt":"2026-09-11T12:03:06","slug":"jase-202612-35-031","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-031","title":{"rendered":"Automated Water Level Measurement Using Intelligent Image Processing of Gauge Scales"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Shoudong Zhou1<\/sup>, Fubao Yang2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>School of Electrical Engineering, Anhui Technical College of Industry and Economy, Hefei 230051, China<\/p>\n\n\n\n

2<\/sup>Anhui Provincial Water Resources and Hydropower Survey, Design and Research Institute Co., Ltd, Hefei 230088, China<\/p>\n<\/div>\n\n\n\n

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Received: May 05, 2026
Accepted: July 29, 2026
Publication Date: September 11, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Convolutional Neural Network Architecture.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.031<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Water level monitoring of rivers, water reservoirs, and dams plays an important role in flood prediction, water resource management, and environmental safety. The conventional water level monitoring techniques include manual observation and sensor-based techniques. The conventional image-based and deep learning-based water level measurement techniques have improved accuracy of the water level measurement system. The dataset used in this research work is the Water Gauge Computer Vision Dataset available in Roboflow Universe, which consists of about 500 real-world water gauge images taken under varying environmental conditions. The proposed water level measurement system differs from the conventional techniques in terms of the usage of YOLOv8-based gauge region detection, hybrid Convolutional Neural Network (CNN)-Optical Character Recognition (OCR) for digit detection, and classical computer vision techniques like edge detection and Hough transform for accurate waterline detection. The experimental results show high detection accuracy with Precision 0.960, Recall 0.955, F1-score 0.958, Accuracy 0.969, and mAP 0.970. The system has high estimation accuracy as well, as shown by MAE 0.036 m, RMSE 0.081 m and MAPE 3.55%, which shows minimal prediction error in the results.<\/p>\n\n\n\n

Keywords: Automated Water Level Monitoring, YOLOv8, Gauge Scale Detection, Computer Vision, CNN-OCR, Image Processing<\/em><\/p>\n\n\n\n

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