UP‐Net: A multi‐head architecture for reading and efficiently segmenting distorted QR codes

Abstract Semantic segmentation is essential in machine vision but susceptible to noise and distortions that often appear in real‐world images. We propose UPlus‐Net (UP‐Net), a deep‐learning architecture based on the U‐Net encoder–decoder architecture. We address the limitations of U‐Net by introducing a multi‐head architecture in UP‐Net to properly handle segmentation challenges. In addition, we evaluate UP‐Net for decoding distorted quick‐response (QR) codes heavily polluted by noise. Experimental results confirm that UP‐Net outperforms existing QR reader mobile applications, highlighting the UP‐Net ability to handle challenging images. Unlike existing methods focused solely on QR code reading or segmentation, UP‐Net offers a combined solution, efficiently and accurately reading distorted QR codes while performing high‐quality semantic segmentation. These unique characteristics render UP‐Net promising for applications demanding robust image analysis in challenging environments.