Open Access

ARTICLE

DM Code Key Point Detection Algorithm Based on CenterNet

Wei Wang¹, Xinyao Tang^2,*, Kai Zhou¹, Chunhui Zhao¹, Changfa Liu³

1 School of Information Engineering, Chang’an University, Xi’an, 710018, China
2 Algorithm Research and Development Department, GRGBanking Equipment Co., Ltd., Guangzhou, 510663, China
3 Research and Development Department, Xi’an Soar Electromechanical Technology, Ltd., Xi’an, 710043, China

* Corresponding Author: Xinyao Tang. Email:

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)

Computers, Materials & Continua 2023, 77(2), 1911-1928. https://doi.org/10.32604/cmc.2023.043233

Received 26 June 2023; Accepted 27 September 2023; Issue published 29 November 2023

Abstract

Data Matrix (DM) codes have been widely used in industrial production. The reading of DM code usually includes positioning and decoding. Accurate positioning is a prerequisite for successful decoding. Traditional image processing methods have poor adaptability to pollution and complex backgrounds. Although deep learning-based methods can automatically extract features, the bounding boxes cannot entirely fit the contour of the code. Further image processing methods are required for precise positioning, which will reduce efficiency. Because of the above problems, a CenterNet-based DM code key point detection network is proposed, which can directly obtain the four key points of the DM code. Compared with the existing methods, the degree of fitness is higher, which is conducive to direct decoding. To further improve the positioning accuracy, an enhanced loss function is designed, including DM code key point heatmap loss, standard DM code projection loss, and polygon Intersection-over-Union (IoU) loss, which is beneficial for the network to learn the spatial geometric characteristics of DM code. The experiment is carried out on the self-made DM code key point detection dataset, including pollution, complex background, small objects, etc., which uses the Average Precision (AP) of the common object detection metric as the evaluation metric. AP reaches 95.80%, and Frames Per Second (FPS) gets 88.12 on the test set of the proposed dataset, which can achieve real-time performance in practical applications.

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Keywords

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