A Fast Adaptive Binarization Method for QR Code Images Based on Dynamic Illumination Equalization

The advancement of Internet of Things (IoT) has enhanced the extensive usage of QR code images in various computer vision applications. Nonetheless, this has also brought forth several technical challenges. In particular, the logistics sorting system often encounters issues such as a low recognition...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2023-10, Vol.12 (19), p.4134
Main Authors: Chen, Rongjun, Huang, Yue, Lan, Kailin, Li, Jiawen, Ren, Yongqi, Hu, Xianglei, Wang, Leijun, Zhao, Huimin, Lu, Xu
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
Decoders
Equalization
Error correction & detection
Food
Illumination
Image acquisition
Image enhancement
Image processing
Internet of Things
Lighting
Localization
Logistics
Machine vision
Mobile commerce
Neighborhoods
Processing speed
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The advancement of Internet of Things (IoT) has enhanced the extensive usage of QR code images in various computer vision applications. Nonetheless, this has also brought forth several technical challenges. In particular, the logistics sorting system often encounters issues such as a low recognition rate and slow processing speed when dealing with QR code images under complex lighting conditions like uneven illumination. To address these difficulties, a method that focuses on achieving a fast adaptive binarization of QR code images through dynamic illumination equalization was proposed. First, an algorithm based on edge enhancement to obtain the position detection patterns within QR code images was applied, which enabled the acquisition of structural features in uneven illumination. Subsequently, QR code images with complex lighting conditions can achieve a fast adaptive binarization through dynamic illumination equalization. As for method validation, the experiments were performed on the two datasets that include QR code images influenced by strong light, weak light, and different shadow degrees. The results disclosed the benefits of the proposed method compared to the previous approaches; it produced superior recognition rates of 78.26–98.75% in various cases through commonly used decoders (Wechat and Zxing), with a faster processing speed of 0.0164 s/image, making it a proper method to satisfy real-time requirements in practical applications, such as a logistics sorting system.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12194134