Abstract
While smart meters are still not widely installed in many countries, automatic reading of traditional-type meters is useful from the perspective of both cost and safety. Although convolutional neural network (CNN) showed a high potential for automatic meter reading under unconstrained environment, it is facing various challenges. One is the difficulty of collecting a sufficient amount of training dataset since some digits of a meter may take a long time to update. Another challenging issue is how to recognize the transitional state between two consecutive numbers. To solve these problems, we propose a new data augmentation technique that can automatically generate annotated images of numbers, including the transitional states. By taking advantage of the state-of-the-art generative neural network model, the generated numbers resemble the local appearance of those in the original meter images. Evaluation experiments confirm that our proposed generative data augmentation techniques improve the robustness of the recognition model and achieve outstanding results when compared to the previous work.
Highlights
Despite of the huge advantages, smart meters are still not widely installed in many countries, especially in the developing one [1]–[3]
Because the last digit is likely to have more variation in numbers in the test dataset, we create the scenario by extracting the meter images whose last digit contains one single number in the training dataset and use these images only to create the training dataset consisting of other numbers at the last digit
Digit Recognition Accuracy (DRA) could reach as high as 85.62 % when use less than 100 images consisting of a single number only for training
Summary
Despite of the huge advantages, smart meters are still not widely installed in many countries, especially in the developing one [1]–[3]. The major contributions of this paper can be summarized as follows: 1) A novel data augmentation method called appearance preserving number generator for automatically generating annotated images of numbers resembling the local appearance of the target digits using a generative neural network. 2) A novel data augmentation method called rotating digit generator for automatically generating annotated images of transitional states between two consecutive numbers. 4) Evaluation experiments using an originally designed deep learning-based AMR framework to demonstrate the effectiveness of the proposed generative data augmentation techniques by comparing with previous works.
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