Using a Fully Connected Convolutional Network to Detect Objects in Images

Alexeev, Alexey; Matveev, Yuri; Kukharev, Georgy

doi:10.1109/snams.2018.8554685

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The training was performed with GPU NVIDIA GEFORCE GTX1080 TI 11 GB GDDR5x. We performed testing with the a3net framework [26]. Full detection of the two used in the work cascades on average took about 3 ms for a gray image of 1024 × 512 size.…”

Section: Methodsmentioning

confidence: 99%

“…The suggested architecture contains non-linear classifiers type NiN [23][24][25], more specifically the solution a3net [26], which follows a particular approach to identitying matrix initialization for building a deep network [27]. The initialization of the identity matrix occurs at the start of the process with the weight (including the weights for the unitary diagonal) changing during the training process.…”

Section: Amr Detectormentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

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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: Methodsmentioning

confidence: 99%

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

Self Cite

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show abstract

“…According to the results, the suggested method is effective in terms of subjective quality, peak signal-to-noise ratio (PSNR), and compression ratio. J. Alexeev, A., Matveev, Y., and Kukharev, G. (2018), In this study [10], an advanced and-new object detection algorithm is proposed that makes use of a neural network with a Network in Network (NiN) type convolution kernel to enable highly parallel processing. Only when the convolution kernel is applied in the form of a fully linked network does its non-linear approach allow for a big stride and the abandonment of pooling.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

Literature Review of Moving Object Detection Using Machine Learning

Kumar¹,

Sinha²,

Kumar³

2022

IJRASET

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In order to answer three different research questions, this paper conducts a literature review on the subject of machine learning in object detection security. The work done covers more than 34 research papers that are relevant to this topic. Charts, tables, and statistics are used to summarize the data and provide the reader with an easily readable summary of the papers that are relevant to this topic.

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Detector of Small Objects with Application to the License Plate Symbols

Alexeev

Matveev

et al. 2019

Advances in Computational Intelligence

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Using a Fully Connected Convolutional Network to Detect Objects in Images

Cited by 3 publications

References 9 publications

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

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

Literature Review of Moving Object Detection Using Machine Learning

Detector of Small Objects with Application to the License Plate Symbols

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