UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes - Ebrahim Parcham; Mahdi Ilbeygi; Vahid Hajipour; Ali Gharaei; Mahdi Mokhtari; Mostafa Foroutan (ETRI Journal)
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 intro-ducing 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 perform-ing high-quality semantic segmentation. These unique characteristics render UP-Net promising for applications demanding robust image analysis in chal-lenging environments.