A Circular Target Feature Detection Framework Based on DCNN for Industrial Applications

This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the ci...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2021-05, Vol.17 (5), p.3303-3313
Main Authors: Tan, Dayu, Chen, Linggang, Jiang, Chao, Zhong, Weimin, Du, Wenli, Qian, Feng, Mahalec, Vladimir
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Background noise
Catalyst particles
deep convolutional neural network (DCNN)
Feature extraction
Field of view
Hough transform
Hough transformation
Industrial applications
industrial circles
Informatics
Machine vision
Object detection
Positioning devices (machinery)
Support vector machines
Target detection
Target recognition
threshold processing
Training
Transforms
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Summary:This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the circle accurately in the image field of view. In this article, a Hough transform based on threshold processing (HTP) is embedded into the optimized DCNN architecture, which calculates the center positions and radius of all circles by training the circular samples for each detected rectangular frame. It can efficiently identify small circular target materials in the industry and screen out unqualified particles. The experimental results show that the boundary information of the circles is obtained clearly from the complex noise background images, thereby accurately determining the location of the circle. It has some advantages over only using a specific circular recognition algorithm. We proposed the new study on HTP-DCNN, which has extremely high accuracy in the field of machine vision positioning with circles for industrial applications.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3024578