Barcode Recognizable System Implementing Based on AM5728
Automation, Control and Intelligent Systems
Volume 4, Issue 6, December 2016, Pages: 89-94
Received: Nov. 29, 2016;
Published: Dec. 1, 2016
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Xicai Li, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Junsheng Shi, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Xiaoqiao Huang, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Yonghang Tai, Color & Image Vision Lab, Yunnan Normal University, Kunming, China; Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
Chongde Zi, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Huan Yang, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Xingyu Yang, Institute of Electronic Science and Engineering, Nanjing University, Nanjing, China
Zhiwei Deng, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
Feiyan Li, Color & Image Vision Lab, Yunnan Normal University, Kunming, China
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To refine the implementation of industrial camera requirements in terms of barcode identification, speeding the barcode image acquisition and processing challenges, as well as the defect of low accuracy. We proposed a barcode recognition framework based on AM5728 embedded system, which employed industrial CCD to scan the barcode image, moreover, integrated with AM5728 visual development platform to manipulate the collected images. After that, decoding information is yielded from series of algorithms refer to convolution filtering, barcode positioning as well as recognition facilitated by AM5728 visual development platform. Experimental outcomes validated that the accuracy of our system recognition rate can reach up to satisfied 100% in the threshold condition, with 20 frames per second barcode images recognition rate.
Barcode, Embedded System, AM5728, Barcode Identification, System Design, Convolution Filtering
To cite this article
Barcode Recognizable System Implementing Based on AM5728, Automation, Control and Intelligent Systems.
Vol. 4, No. 6,
2016, pp. 89-94.
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