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Open AccessArticle

A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network

School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
Authors to whom correspondence should be addressed.
Electronics 2019, 8(8), 825;
Received: 26 June 2019 / Revised: 20 July 2019 / Accepted: 22 July 2019 / Published: 25 July 2019
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. In this paper, a fast recognition method for electronic components in a complex background is presented. Firstly, we built the image dataset, including image acquisition, image augmentation, and image labeling. Secondly, a fast recognition method based on deep learning was proposed. The balance between detection accuracy and detection speed was solved through the lightweight improvement of YOLO (You Only Look Once)-V3 network model. Finally, the experiment was completed, and the proposed method was compared with several popular detection methods. The results showed that the accuracy reached 95.21% and the speed was 0.0794 s, which proved the superiority of this method for electronic component detection. View Full-Text
Keywords: rapid recognition; machine vision; deep learning; YOLO-V3 rapid recognition; machine vision; deep learning; YOLO-V3
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Huang, R.; Gu, J.; Sun, X.; Hou, Y.; Uddin, S. A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network. Electronics 2019, 8, 825.

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