{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:23:35Z","timestamp":1781652215969,"version":"3.54.5"},"reference-count":90,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council of Thailand (NRCT)","award":["NRCT5-RSA63004-05"],"award-info":[{"award-number":["NRCT5-RSA63004-05"]}]},{"name":"Chiang Mai University (CMU)","award":["NRCT5-RSA63004-05"],"award-info":[{"award-number":["NRCT5-RSA63004-05"]}]},{"DOI":"10.13039\/501100004704","name":"College of Arts, Media, and Technology (CAMT)","doi-asserted-by":"publisher","award":["NRCT5-RSA63004-05"],"award-info":[{"award-number":["NRCT5-RSA63004-05"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model\u2019s performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.<\/jats:p>","DOI":"10.3390\/s22228788","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:36:40Z","timestamp":1668479800000},"page":"8788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7294-150X","authenticated-orcid":false,"given":"Teerawat","family":"Kamnardsiri","sequence":"first","affiliation":[{"name":"Department of Digital Game, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-6856","authenticated-orcid":false,"given":"Phasit","family":"Charoenkwan","sequence":"additional","affiliation":[{"name":"Department of Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"},{"name":"A Research Group of Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5164-8398","authenticated-orcid":false,"given":"Chommaphat","family":"Malang","sequence":"additional","affiliation":[{"name":"Department of Digital Industry Integration, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1907-9444","authenticated-orcid":false,"given":"Ratapol","family":"Wudhikarn","sequence":"additional","affiliation":[{"name":"A Research Group of Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"},{"name":"Department of Knowledge and Innovation Management, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. 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