Detection and recognition of multiple QR codes based on YOLO_CBAM algorithm Online publication date: Mon, 08-Apr-2024
by Juntao Li; Meijuan Zhao; Zhenbo Qin; Ruiping Yuan; Anqiang Huang; Mengtao Li
International Journal of Bio-Inspired Computation (IJBIC), Vol. 23, No. 3, 2024
Abstract: Different forms of two-dimensional codes are already widely used in all aspects of people's production and life, and the demand for rapid detection and recognition technology of multiple QR codes in various complex scenarios is also increasing. The traditional QR code detection algorithm has a high miss detection rate. In this paper, we collected a little sample data of multiple QR codes and annotated them. We used the YOLOv3 and YOLOv5 algorithms to implement the detection of multiple QR codes. Then, we added CBAM to the YOLO algorithm, and added an angle prediction mechanism to improve their decoding and recognition effects. The experimental results showed that the YOLOv3_CBAM algorithm and the YOLOv5_CBAM algorithm improved by 0.63% and 8.89% about mAP@.5 and mAP@.5:.95 respectively for the multiple QR codes dataset, with a detection speed of 70 FPS and achieved real-time performance.
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