HRC-mCNNs: A Hybrid Regression and Classification Multibranch CNNs for Automatic Meter Reading With Smart Shell

Nowadays, the meter reading is detached into two parts: 1) image collection and 2) image recognition. Most previous researchers perceived meter reading as an image classification problem and obtained impressive classification performance. However, the numerical is also a critical metric of meter measurement and has not been noticed in previous research. This article redefines the meter reading issue as a hybrid procedure of regression and classification and creates a specific model. The resulting algorithm bespeaks the performance of measurement and recognition. The model consists of a hybrid regression and classification loss function and multibranch convolutional neural networks. We construct two data sets to validate the model: 1) normal data set and 2) carry data set, corresponding to classification and numeric accuracy dividedly. The experiments show that the model establishes new state-of-the-art metrics and achieves 0.5312 mean square error (MSE) on numerical precision and 99.98% accuracy on classification accuracy for 3-min training, by over 70 times on MSE and 0.11% on accuracy than the best performing model proposed by a recent study. Furthermore, a production-ready meter-reading system was deployed in a genuine factory with hybrid regression and classification multibranch convolutional neural networks, smart meter shells, and a set of cloud servers.

Automated summary

This article addresses the issue of numerical metrics in meter reading and proposes a hybrid regression and classification model. The experiments show that the model achieves good performance in numerical precision and classification accuracy.