Comparative Analysis of Deep Learning Barcode Decoding: Evaluating WeChatQRCode vs Pyzbar in Unconstrained Environments

Abstract

This research introduces a novel hybrid framework integrating YOLO-v5 object detection with Convolutional Neural Network (CNN)-based decoding to address severe visual degradation in unconstrained logistics environments. Compared to standard detection-decoding pipelines (YOLO+Pyzbar) reporting a 91% accuracy rate under general conditions, this study identifies and quantifies the “Cliff Effect” in traditional heuristic algorithms, which exhibit failure once Gaussian noise exceeds the critical threshold of σ=25. In contrast, the implementation of WeChatQRCode demonstrates absolute robustness with a 100% Success Rate and deterministic latency. The novelty of this work lies in proving that neural decoding provides a superior reliability foundation for Industry 4.0 than classic scanline methods under extreme signal degradation.