Detector of Small Objects with Application to the License Plate Symbols

Alexeev, Alexey; Matveev, Yuri; Matveev, Anton; Kukharev, Georgy; Almatarneh, Sattam

doi:10.1007/978-3-030-20521-8_44

Cited by 1 publication

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“…Also, even for deep-architectures we do not use skip-connections. Non-linear classifiers perform exceptionally well for preserving spacial information until the final convolution layers comparing to linear classifiers with pooling, commonly used in CNN, and at the same time, larger stride allows, in spite of the use of the non-linear kernel, to improve performance for the same degree of the capacity C of the neural network [28].…”

Section: Amr Detectormentioning

confidence: 99%

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

Alexeev¹,

Kukharev

Matveev

et al. 2020

Mathematics

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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.

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Section: Amr Detectormentioning

confidence: 99%