使用SSD检测电表中的数字区域

Chun-Ming Tsai, T. Shou, Shao-Chi Chen, J. Hsieh
{"title":"使用SSD检测电表中的数字区域","authors":"Chun-Ming Tsai, T. Shou, Shao-Chi Chen, J. Hsieh","doi":"10.1109/ICMLC48188.2019.8949195","DOIUrl":null,"url":null,"abstract":"Every two months, the Taiwan Power Company will dispatch staffs to each household to read numbers in electricity meters to calculate and collect electricity bills. However, these electricity meter staff sometimes read the wrong meter numbers and so calculate the wrong electricity bill. A system that automatically detects the digital region in electricity meter, could reduce this misreading of numbers and calculate the electricity bill correctly, thereby increasing work efficiency. Herein, the deep learning model SSD (Single Shot MultiBox Detector) is applied and fine-turned to detect the digital region in electricity meter to help the Taiwan Power Company staff. From the experimental results, it is demonstrated that the presented deep learning methods detect the digital region better than the pre-trained SSD model. In the testing experiments, the accuracies of the digital region detection are 100% for both our collected data's and fine-tuned SSD, respectively.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Use SSD to Detect the Digital Region in Electricity Meter\",\"authors\":\"Chun-Ming Tsai, T. Shou, Shao-Chi Chen, J. Hsieh\",\"doi\":\"10.1109/ICMLC48188.2019.8949195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Every two months, the Taiwan Power Company will dispatch staffs to each household to read numbers in electricity meters to calculate and collect electricity bills. However, these electricity meter staff sometimes read the wrong meter numbers and so calculate the wrong electricity bill. A system that automatically detects the digital region in electricity meter, could reduce this misreading of numbers and calculate the electricity bill correctly, thereby increasing work efficiency. Herein, the deep learning model SSD (Single Shot MultiBox Detector) is applied and fine-turned to detect the digital region in electricity meter to help the Taiwan Power Company staff. From the experimental results, it is demonstrated that the presented deep learning methods detect the digital region better than the pre-trained SSD model. In the testing experiments, the accuracies of the digital region detection are 100% for both our collected data's and fine-tuned SSD, respectively.\",\"PeriodicalId\":221349,\"journal\":{\"name\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC48188.2019.8949195\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC48188.2019.8949195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

每两个月,台湾电力公司会派工作人员到每家每户读电表上的数字,计算和收取电费。然而,这些电表工作人员有时会读错电表号码,从而计算出错误的电费。在电表中自动检测数字区域的系统,可以减少数字的误读,正确计算电费,从而提高工作效率。本文运用深度学习模型SSD (Single Shot MultiBox Detector)对电能表中的数字区域进行精细检测,以帮助台湾电力公司的工作人员。实验结果表明,所提出的深度学习方法比预训练的SSD模型更好地检测数字区域。在测试实验中,我们采集的数据和调优的SSD的数字区域检测准确率分别为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Use SSD to Detect the Digital Region in Electricity Meter
Every two months, the Taiwan Power Company will dispatch staffs to each household to read numbers in electricity meters to calculate and collect electricity bills. However, these electricity meter staff sometimes read the wrong meter numbers and so calculate the wrong electricity bill. A system that automatically detects the digital region in electricity meter, could reduce this misreading of numbers and calculate the electricity bill correctly, thereby increasing work efficiency. Herein, the deep learning model SSD (Single Shot MultiBox Detector) is applied and fine-turned to detect the digital region in electricity meter to help the Taiwan Power Company staff. From the experimental results, it is demonstrated that the presented deep learning methods detect the digital region better than the pre-trained SSD model. In the testing experiments, the accuracies of the digital region detection are 100% for both our collected data's and fine-tuned SSD, respectively.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Empirical Study on the Classification of Chinese News Articles by Machine Learning and Deep Learning Techniques Posture Estimation Method Using Cushion Type Seat Pressure Sensor Advanced Convolutional Neural Network With Feedforward Inhibition Utilization of the Infrared Image Capturing Combustion State for Estimating the Steam Flow Aming to Stabilize Garbage Power Generation Domain Adaption for Facial Expression Recognition
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1