{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:01:15Z","timestamp":1784289675519,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Science and technology planning project","award":["No. Z201100001820022"],"award-info":[{"award-number":["No. Z201100001820022"]}]},{"name":"Beijing Science and technology planning project","award":["No. SKL2020TS01"],"award-info":[{"award-number":["No. SKL2020TS01"]}]},{"name":"Beijing Science and technology planning project","award":["No. 0704183"],"award-info":[{"award-number":["No. 0704183"]}]},{"name":"Beijing Science and technology planning project","award":["No. YSPTZX202142"],"award-info":[{"award-number":["No. YSPTZX202142"]}]},{"name":"Beijing Science and technology planning project","award":["No. U1865102"],"award-info":[{"award-number":["No. U1865102"]}]},{"name":"the free exploration topic of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["No. Z201100001820022"],"award-info":[{"award-number":["No. Z201100001820022"]}]},{"name":"the free exploration topic of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["No. SKL2020TS01"],"award-info":[{"award-number":["No. SKL2020TS01"]}]},{"name":"the free exploration topic of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["No. 0704183"],"award-info":[{"award-number":["No. 0704183"]}]},{"name":"the free exploration topic of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["No. YSPTZX202142"],"award-info":[{"award-number":["No. YSPTZX202142"]}]},{"name":"the free exploration topic of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin","award":["No. U1865102"],"award-info":[{"award-number":["No. U1865102"]}]},{"name":"Scientific Research Project of China Three Gorges Corporation","award":["No. Z201100001820022"],"award-info":[{"award-number":["No. Z201100001820022"]}]},{"name":"Scientific Research Project of China Three Gorges Corporation","award":["No. SKL2020TS01"],"award-info":[{"award-number":["No. SKL2020TS01"]}]},{"name":"Scientific Research Project of China Three Gorges Corporation","award":["No. 0704183"],"award-info":[{"award-number":["No. 0704183"]}]},{"name":"Scientific Research Project of China Three Gorges Corporation","award":["No. YSPTZX202142"],"award-info":[{"award-number":["No. YSPTZX202142"]}]},{"name":"Scientific Research Project of China Three Gorges Corporation","award":["No. U1865102"],"award-info":[{"award-number":["No. U1865102"]}]},{"name":"Scientific Research Special Project of Academician Innovation Platform of Hainan Province","award":["No. Z201100001820022"],"award-info":[{"award-number":["No. Z201100001820022"]}]},{"name":"Scientific Research Special Project of Academician Innovation Platform of Hainan Province","award":["No. SKL2020TS01"],"award-info":[{"award-number":["No. SKL2020TS01"]}]},{"name":"Scientific Research Special Project of Academician Innovation Platform of Hainan Province","award":["No. 0704183"],"award-info":[{"award-number":["No. 0704183"]}]},{"name":"Scientific Research Special Project of Academician Innovation Platform of Hainan Province","award":["No. YSPTZX202142"],"award-info":[{"award-number":["No. YSPTZX202142"]}]},{"name":"Scientific Research Special Project of Academician Innovation Platform of Hainan Province","award":["No. U1865102"],"award-info":[{"award-number":["No. U1865102"]}]},{"name":"National Natural Science Foundation of China","award":["No. Z201100001820022"],"award-info":[{"award-number":["No. Z201100001820022"]}]},{"name":"National Natural Science Foundation of China","award":["No. SKL2020TS01"],"award-info":[{"award-number":["No. SKL2020TS01"]}]},{"name":"National Natural Science Foundation of China","award":["No. 0704183"],"award-info":[{"award-number":["No. 0704183"]}]},{"name":"National Natural Science Foundation of China","award":["No. YSPTZX202142"],"award-info":[{"award-number":["No. YSPTZX202142"]}]},{"name":"National Natural Science Foundation of China","award":["No. U1865102"],"award-info":[{"award-number":["No. U1865102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing water gauge reading approaches based on image analysis have problems such as poor scene adaptability and weak robustness. Here, we proposed a novel water level measurement method based on deep learning (YOLOv5s, convolutional neural network) to overcome these problems. The proposed method uses the YOLOv5s to extract the water gauge area and all scale character areas in the original video image, uses image processing technology to identify the position of the water surface line, and then calculates the actual water level elevation. The proposed method is validated with a video monitoring station on a river in Beijing, and the results show that the systematic error of the proposed method is only 7.7 mm, the error is within 1 cm\/the error is between 1 cm and 3 cm, and the proportion of the number of images is 95%\/5% (daylight), 98%\/2% (infrared lighting at night), 97%\/2% (strong light), 45%\/44% (transparent water body), 91%\/9% (rainfall), and 90%\/10% (water gauge is slightly dirty). The results demonstrate that the proposed method shows good performance in different scenes, and its effectiveness has been confirmed. At the same time, it has a strong robustness and provides a certain reference for the application of deep learning in the field of hydrological monitoring.<\/jats:p>","DOI":"10.3390\/s22103714","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Water Level Measurement Approach Based on YOLOv5s"],"prefix":"10.3390","volume":"22","author":[{"given":"Guangchao","family":"Qiao","sequence":"first","affiliation":[{"name":"College of New Energy and Environment, Jilin University, No. 2519, Jiefang Road, Chaoyang District, Changchun 130000, China"},{"name":"China Institute of Water Resources and Hydropower Research, No. 1 Yuyuantan South Road, Haidian District, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, No. 1 Yuyuantan South Road, Haidian District, Beijing 100038, China"},{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, No. 1 Yuyuantan South Road, Haidian District, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, No. 1 Yuyuantan South Road, Haidian District, Beijing 100038, China"},{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, No. 1 Yuyuantan South Road, Haidian District, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/JSEN.2021.3132098","article-title":"Polymer Optical Fiber Liquid Level Sensor: A Review","volume":"22","author":"He","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2019WR026810","DOI":"10.1029\/2019WR026810","article-title":"A Technical Evaluation of Lidar-Based Measurement of River Water Levels","volume":"56","author":"Paul","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, S., Duan, Q., Chu, X., and Yang, C. (2019). Fluviograph Design Based on an Ultra-Small Pressure Sensor. Sensors, 19.","DOI":"10.3390\/s19214615"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1109\/TMI.2021.3060497","article-title":"Learning With Context Feedback Loop for Robust Medical Image Segmentation","volume":"40","author":"Girum","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106617","DOI":"10.1016\/j.knosys.2020.106617","article-title":"A deep learning based image enhancement approach for autonomous driving at night","volume":"213","author":"Li","year":"2020","journal-title":"Knowledge-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107724","DOI":"10.1016\/j.patcog.2020.107724","article-title":"Face illumination recovery for the deep learning feature under severe illumination variations","volume":"111","author":"Hu","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhou, Y., Liu, H., Zhang, L., and Wang, H. (2019). Visual Measurement of Water Level under Complex Illumination Conditions. Sensors, 19.","DOI":"10.3390\/s19194141"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10362","DOI":"10.1029\/2018WR023913","article-title":"Automatic Image-Based Water Stage Measurement for Long-Term Observations in Ungauged Catchments","volume":"54","author":"Eltner","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20006","DOI":"10.3390\/s150820006","article-title":"Visual Sensing for Urban Flood Monitoring","volume":"15","author":"Lo","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104200Q","DOI":"10.1117\/12.2281594","article-title":"Rapid pedestrian detection algorithm based on deformable part model","volume":"10420","author":"Chai","year":"2017","journal-title":"Proc. SPIE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_13","first-page":"91","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Processing Syst. (NIPS)"},{"key":"ref_14","first-page":"379","article-title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","volume":"29","author":"Dai","year":"2016","journal-title":"Adv. Neural Inf. Processing Syst. (NIPS)"},{"key":"ref_15","unstructured":"Devries, T., and Taylor, G.W. (2017). Improved regularization of convolutional neural networks with CutOut. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_18","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_19","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0031-3203(91)90073-E","article-title":"A probabilistic Hough transform","volume":"24","author":"Kiryati","year":"1991","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/TVT.2020.3044994","article-title":"A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian\/Non-Gaussian Impulsive Noise","volume":"70","author":"Wang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_25","first-page":"012028","article-title":"A Novel approach in Hybrid Median Filtering for Denoising Medical images","volume":"1187","author":"Seetharaman","year":"2021","journal-title":"Mater. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, J., Guo, H., Cheng, W., Pan, T., and Yang, W. (2019). Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11242930"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107929","DOI":"10.1016\/j.patcog.2021.107929","article-title":"STDnet-ST: Spatio-temporal ConvNet for small object detection","volume":"116","author":"Bosquet","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108117","DOI":"10.1016\/j.patcog.2021.108117","article-title":"Correlation-based Structural Dropout for Convolutional Neural Networks","volume":"120","author":"Zeng","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107816","DOI":"10.1016\/j.patcog.2021.107816","article-title":"IoU-uniform R-CNN: Breaking through the limitations of RPN","volume":"112","author":"Zhu","year":"2021","journal-title":"Pattern Recognit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3714\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:15Z","timestamp":1760137815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3714"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103714"],"URL":"https:\/\/doi.org\/10.3390\/s22103714","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,13]]}}}