Optimizing Barcode Detection and Recognition with YOLOv5 and Dynamic Barcode Reader Techniques

This paper presents a comprehensive approach to barcode detection and recognition, employing the YOLOv5s model for precise localization and the Dynamic Barcode Reader for accurate decoding. The proposed system is designed to address the complexities of barcode detection and recognition in varied environments, including challenging conditions such as inconsistent lighting, diverse viewing angles, and fluctuations in barcode quality. A specialized dataset consisting of 3,425 images was utilized for training and evaluation purposes. The YOLOv5s model demonstrated a detection accuracy of 92.61 %, while the Dynamic Barcode Reader algorithm achieved a recognition accuracy of 98%. These findings underscore the system's robustness and effectiveness in real-world applications, highlighting its potential for deployment in sectors that demand swift and reliable barcode processing, such as retail, logistics, and warehousing.