A Highly Efficient Neural Network Solution for Automated Detection of Pointer Meters with Different Analog Scales Operating in Different Conditions

Alexey Alexeev, Georgy Kukharev, Yuri Matveev and Anton Matveev
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Alexey Alexeev: Huawei Research Center, 197022 Saint Petersburg, Russia
Georgy Kukharev: Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, 197022 Saint Petersburg, Russia
Yuri Matveev: Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia
Anton Matveev: Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia

Mathematics, 2020, vol. 8, issue 7, 1-13

Abstract: We investigate a neural network–based solution for the Automatic Meter Reading detection problem, applied to analog dial gauges. We employ a convolutional neural network with a non-linear Network in Network kernel. Presently, there is a significant interest in systems for automatic detection of analog dial gauges, particularly in the energy and household sectors, but the problem is not yet sufficiently addressed in research. Our method is a universal three-level model that takes an image as an input and outputs circular bounding areas, object classes, grids of reference points for all symbols on the front panel of the device and positions of display pointers. Since all analog pointer meters have a common nature, this multi-cascade model can serve various types of devices if its capacity is sufficient. The model is using global regression for locations of symbols, which provides resilient results even for low image quality and overlapping symbols. In this work, we do not focus on the pointer location detection since it heavily depends on the shape of the pointer. We prepare training data and benchmark the algorithm with our own framework a3net, not relying on third-party neural network solutions. The experimental results demonstrate the versatility of the proposed methods, high accuracy, and resilience of reference points detection.

Keywords: AMR; dial gauge; pointer meter; object detection; CNN; NiN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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