{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:15:07Z","timestamp":1760058907688,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Sichuan Electric Power Company Science and Technology Program","award":["521997230014"],"award-info":[{"award-number":["521997230014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In practical applications, the clarity of analog dial images is often compromised due to factors such as lighting conditions, leading to low precision and poor segmentation of dial scales and pointers. This results in segmentation outcomes that fail to meet the real-time requirements of substation inspection systems. To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. Additionally, we replace traditional convolution operations with wavelet convolution, which improves the network\u2019s ability to capture low-frequency information, essential for understanding the overall dial structure. An adaptive attention mechanism is also incorporated in the upsampling stage of the network, enabling the model to dynamically focus on salient features, further improving generalization. These improvements enable the network to more accurately detect target regions within dial images, significantly enhancing segmentation accuracy and robustness. Experimental results demonstrate that the proposed method outperforms traditional U-Net models in segmentation tasks, achieving superior precision in segmenting scales and pointers, effectively addressing issues of low precision and poor segmentation, and making it suitable for real-time substation inspection systems.<\/jats:p>","DOI":"10.3390\/info16050382","type":"journal-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T20:10:27Z","timestamp":1746389427000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness"],"prefix":"10.3390","volume":"16","author":[{"given":"Wan","family":"Zou","sequence":"first","affiliation":[{"name":"State Grid Sichuan Electric Power Company, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Company, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Liao","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhai","family":"Fan","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueping","family":"Yang","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"key":"ref_1","first-page":"54","article-title":"Research Status and Development of Automatic Reading Technology for Pointer Instruments","volume":"45","author":"Han","year":"2018","journal-title":"Comput. Sci."},{"key":"ref_2","first-page":"174","article-title":"Pointer meter detection method based on artificial-real sample metric learning","volume":"59","author":"Zhai","year":"2022","journal-title":"Electr. Meas. Instrum."},{"key":"ref_3","unstructured":"Yang, Y.Q., Zhao, Y.Q., He, X.Y., and Tian, P. (,  2001). Automatic Calibration of Analog Measuring Instruments Using Computer Vision. Proceedings of the 3rd Youth Academic Conference of the China Instrument and Control Society (Volume I), North China Electric Power University, Beijing, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fang, Y.X., Dai, Y., He, G.L., and Qi, D. (2019, January 27\u201330). A mask RCNN based automatic reading method for pointer meter. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8865369"},{"key":"ref_5","first-page":"3097","article-title":"Automatic Reading Method for Pointer Instruments in Substations Based on Faster R-CNN and U-Net","volume":"44","author":"Wan","year":"2020","journal-title":"Power Syst. Technol."},{"key":"ref_6","first-page":"232","article-title":"A Reading Recognition Method for Pointer Instruments in Substations Based on CenterNet and DeepLabv3+","volume":"37","author":"Huang","year":"2022","journal-title":"J. Electr. Power"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1109\/JSEN.2012.2231940","article-title":"Architecture of Noninvasive Real-Time Visual Monitoring System for Dial Type Measuring Instrument","volume":"13","author":"Jaffery","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1108\/AEAT-06-2021-0191","article-title":"Determining the Pointer Positions of Aircraft Analog Indicators Using Deep Learning","volume":"94","author":"Tunca","year":"2022","journal-title":"Aircraft Eng. Aeros. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3488147","article-title":"Robust Pointer Meter Reading Recognition Method Under Image Corruption","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hou, Q.B., Zhang, L., Cheng, M.M., and Feng, J.S. (2020, January 13\u201319). Strip Pooling: Re-thinking Spatial Pooling for Scene Parsing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00406"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","unstructured":"Yang, L., Zhang, R.Y., Li, L., and Xie, X. (2021, January 18\u201324). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107139","DOI":"10.1016\/j.engappai.2023.107139","article-title":"Dual Cross-Attention for Medical Image Segmentation","volume":"126","author":"Ates","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., and Bhattacharyya, S.P. (2023, January 17\u201324). PIDNet: A real-time semantic segmentation network inspired by PID controllers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A ConvNet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/382\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:26:46Z","timestamp":1760030806000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,3]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["info16050382"],"URL":"https:\/\/doi.org\/10.3390\/info16050382","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,5,3]]}}}