UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes
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 mul...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Electronics and Telecommunications Research Institute (ETRI)
2025-06-01
|
| Series: | ETRI Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.4218/etrij.2023-0540 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849710662159171584 |
|---|---|
| author | Ebrahim Parcham Mahdi Ilbeygi Vahid Hajipour Ali Gharaei Mahdi Mokhtari Mostafa Foroutan |
| author_facet | Ebrahim Parcham Mahdi Ilbeygi Vahid Hajipour Ali Gharaei Mahdi Mokhtari Mostafa Foroutan |
| author_sort | Ebrahim Parcham |
| collection | DOAJ |
| description | 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.
|
| format | Article |
| id | doaj-art-4f353bcb3f844802ae88f0acde78bd1f |
| institution | DOAJ |
| issn | 1225-6463 2233-7326 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj-art-4f353bcb3f844802ae88f0acde78bd1f2025-08-20T03:14:50ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262025-06-0147352754410.4218/etrij.2023-0540UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codesEbrahim ParchamMahdi IlbeygiVahid HajipourAli Gharaei Mahdi MokhtariMostafa ForoutanSemantic 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. https://doi.org/10.4218/etrij.2023-0540image distortionmobile phonemulti-head architectureqr code readersemantic segmentation |
| spellingShingle | Ebrahim Parcham Mahdi Ilbeygi Vahid Hajipour Ali Gharaei Mahdi Mokhtari Mostafa Foroutan UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes ETRI Journal image distortion mobile phone multi-head architecture qr code reader semantic segmentation |
| title | UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes |
| title_full | UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes |
| title_fullStr | UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes |
| title_full_unstemmed | UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes |
| title_short | UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes |
| title_sort | up net a multi head architecture for reading and efficiently segmenting distorted qr codes |
| topic | image distortion mobile phone multi-head architecture qr code reader semantic segmentation |
| url | https://doi.org/10.4218/etrij.2023-0540 |
| work_keys_str_mv | AT ebrahimparcham upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes AT mahdiilbeygi upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes AT vahidhajipour upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes AT aligharaei upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes AT mahdimokhtari upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes AT mostafaforoutan upnetamultiheadarchitectureforreadingandefficientlysegmentingdistortedqrcodes |