A. E. Averyanikhin, A. I. Vlasov, E. V. Evdokimova Department IU4 of Designing and Technology of Electronic Equipment, Bauman Moscow State Technical University (Moscow, Russia)

The main problem of known deep convolutional neural networks (CNN) is that they require a fixed-size input image. This requirement is “artificial” and can reduce recognition accuracy for images or its parts of arbitrary size/scale. The paper proposes a strategy of combining “hierarchical pyramidal subselection” to eliminate the above restriction. The structure of the neural network using the proposed combining strategy allows the generation of prediction regardless of the size/scale of the original image, and also improves the accuracy of recognition.

Features of application of CNN for identification and recognition of defects of conducting pattern of printed circuit board blanks have been considered. Features of defects of conductive pattern of printed circuit board blanks have been briefly discussed. The invention proposes the use of artificial CNN, which have advantages in speed and accuracy in solving problems of object recognition on images relative to existing methods. The focus is on the architecture of CNN using hierarchical pyramidal subselection. Capabilities of application of CNN for recognition of defects of conducting pattern of printed circuit board blanks have been shown.

Proposed method of hierarchical pyramidal subselection in deep convolutional networks has been implemented in software complex, which allows processing digital data of photographs of conducting pattern of printed circuit boards, in particular during their flaw detection, and can be used for localization of existing defects of conducting pattern. The conclusion draws the possibilities of using methods and means of image processing in flaw detection of radio-electronic equipment and instruments

Averyanikhin A.E., Vlasov A.I., Evdokimova E.V. Hierarchical pyramidal subsampling in deep convolutional networks for visual pattern recognition. Neurocomputers. 2021. Vol. 23. No. 1. P. 17–31. DOI: 10.18127/j19998554-202101-03. (in Russian)

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