Original scientific paper
https://doi.org/10.1080/00051144.2024.2362575
Recognition and analysis system of steel stamping character based on machine vision
Chen Wenming
; School of Artificial Intelligence, Ningbo Polytechnic, Zhejiang, People’s Republic of China
*
Tong Tianhong
; Faculty of Mechanical Engineering&Mechanics, Ningbo University, Ningbo, People’s Republic of China
Liang Dongtai
; Faculty of Mechanical Engineering&Mechanics, Ningbo University, Ningbo, People’s Republic of China
Xu Hangbin
; Faculty of Mechanical Engineering&Mechanics, Ningbo University, Ningbo, People’s Republic of China
Chen Zizhen
; School of Artificial Intelligence, Ningbo Polytechnic, Zhejiang, People’s Republic of China
Sun Haoming
; Beilun Ore Terminal Branch, Ningbo Zhoushan Port Co., Ningbo, People’s Republic of China
* Corresponding author.
Abstract
During the packaging process, it is essential to detect the steel stamping characters inside the
box to identify any missing or repeated characters. Currently, manual detection suffers from low
efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced
YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue
of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of
the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of
the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was
used to improve the efficiency of mapping steel stamping character recognition sequences. The
experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with
a processing efficiency of 10.5 ms per single frame. These findings align with the performance
criteria for automatic recognition and analysis of steel stamping characters.
Keywords
Machine vision detection system; steel stamping character recognition; YOLOv7; multi-scale detection; location attention; hash table
Hrčak ID:
326275
URI
Publication date:
4.6.2024.
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