Use SSD to Detect the Digital Region in Electricity Meter

Chun-Ming Tsai, T. Shou, Shao-Chi Chen, J. Hsieh
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引用次数: 10

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.
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使用SSD检测电表中的数字区域
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%。
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