A Gradient Method for Generating Facial Barcodes

Kukharev, Georgy; Matveev, Yuri; Shchegoleva, Nadezhda

doi:10.1007/978-3-319-12580-0_12

Cited by 3 publications

(2 citation statements)

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“…The points marked in gray are inactive agents and do not used further since the object is not included in their activation area, which in turn is determined by some given radius. In the work, it was defined as (x i − x i+1 ) 2 + (y i − y i+1 ) 2 , where x i and y i are coordinates of points in the reference grid. The reference points are evenly spread in a checkerboard pattern in the center of the detection area.…”

Section: Localization Of the Objectsmentioning

confidence: 99%

“…Specifically, analog dial gauges are comprised of dials, pointers and symbols. The achievements of this work could be translated to other types of objects, for example when a regressive generation of a grid of reference points is needed for facial landmark detection [1,2]. Resilient generation of an object's grid is also important for other problems and in this work we describe our procedures for training data preparation; the structure of the neural networks, which are combined in a cascade; and the training procedure.…”

Section: Introductionmentioning

confidence: 99%

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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: Localization Of the Objectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%