Research Paper:
Label Design and Extraction in High-Temperature Logistics Based on Concave Coding and MLFFA-DeepLabV3+ Network
Xiaoyan Zhao*1,*2,†
, Pengfei Zhao*2, Yuguo Yin*3, Luqi Tao*1, Jianfeng Yan*4, and Zhaohui Zhang*1,*2
*1School of Automation and Electrical Engineering, University of Science and Technology Beijing
30 Xueyuan Road, Haidian District, Beijing 100083, China
*2Shunde Innovation School, University of Science and Technology Beijing
2 Zhihui Road, Daliang, Shunde District, Fo Shan, Guangdong 528399, China
*3Shandong Start Measurement and Control Equipment Co., Ltd.
600 Xinyi Road, Weifang Economic Development Zone, Weifang, Shandong 261101, China
*4Future Development Research Center, China Ship Research and Development Academy
2 Shuangquanpu, Chaoyang District, Beijing 100192, China
†Corresponding author
Logistics tracking technology at normal temperature is quite mature, but there are few tracking methods for the high-temperature production process. The main difficulties are that the label materials generally used cannot withstand the high temperature for a long time, and the detection devices are vulnerable to environmental impact. A high-temperature logistics tracking solution was developed for a carbon anode used in an aluminum electrolysis factory. It is based on concave coding and a multiscale low-level feature fusion and attention-DeepLabV3+ (MLFFA-DeepLabV3+) network extraction technique for the coded region of the concave coding. The concave coding is printed on the product as a tag that can endure a high temperature of more than 1,200°C, ensuring its integrity and identifiability. Because there is no obvious color distinction between the coding area and the background, direct recognition is ineffective. The MLFFA-DeepLabV3+ network extracts the coding region to improve the recognition rate. The DeepLabV3+ network is improved by replacing the backbone network and adding of a multiscale low-level feature fusion module and convolutional block attention module. Experimental results showed that the mean pixel accuracy and mean intersection over union of the MLFFA-DeepLabV3+ network increased by 2.37% and 2.45%, respectively, compared with the original DeepLabV3+ network. The network structure has only 11.24% of the number of parameters in the original structure. The solution is feasible and provides a basis for high-temperature logistics tracking technology in intelligent manufacturing.
Concave coding example
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