{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:07:44Z","timestamp":1769710064344,"version":"3.49.0"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST-110-2927-I-324-50"],"award-info":[{"award-number":["MOST-110-2927-I-324-50"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST-110-2221-E-324-010"],"award-info":[{"award-number":["MOST-110-2221-E-324-010"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST-109-2622-E-324-004"],"award-info":[{"award-number":["MOST-109-2622-E-324-004"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vietnam J. Comp. Sci."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p> Recently, it was shown that convolutional neural networks (CNNs) with suitably annotated training data and results produce the best traffic sign detection (TSD) and recognition (TSR). The whole system\u2019s efficiency is determined by the data collecting process based on neural networks. As a result, the datasets for traffic signs in most nations throughout the globe are difficult to recognize because of their diversity. To address this problem, we must create a synthetic image to enhance our dataset. We apply deep convolutional generative adversarial networks (DCGAN) and Wasserstein generative adversarial networks (Wasserstein GAN, WGAN) to generate realistic and diverse additional training images to compensate for the original image distribution\u2019s data shortage. This study focuses on the consistency of DCGAN and WGAN images created with varied settings. We utilize an actual picture with various numbers and scales for training. Additionally, the Structural Similarity Index (SSIM) and the Mean Square Error (MSE) were used to determine the image\u2019s quality. In our study, we computed the SSIM values between pictures and their corresponding real images. When more training images are used, the images created have a significant degree of similarity to the original image. The results of our experiment reveal that the most leading SSIM values are achieved when 200 total images of [Formula: see text] pixels are utilized as input and the epoch is 2000. <\/jats:p>","DOI":"10.1142\/s2196888822500191","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T13:36:31Z","timestamp":1648474591000},"page":"333-348","source":"Crossref","is-referenced-by-count":8,"title":["Synthetic Traffic Sign Image Generation Applying Generative Adversarial Networks"],"prefix":"10.1142","volume":"09","author":[{"given":"Christine","family":"Dewi","sequence":"first","affiliation":[{"name":"Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan, R.O.C."},{"name":"Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rung-Ching","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan-Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,3,26]]},"reference":[{"key":"S2196888822500191BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09509-x"},{"key":"S2196888822500191BIB002","first-page":"1","volume":"18","author":"Suthamathi V.","year":"2020","journal-title":"Int. J. Appl. Sci. Eng."},{"key":"S2196888822500191BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"S2196888822500191BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.232"},{"key":"S2196888822500191BIB005","doi-asserted-by":"publisher","DOI":"10.1145\/3408066.3408078"},{"key":"S2196888822500191BIB006","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9060889"},{"key":"S2196888822500191BIB007","first-page":"237","volume":"17","author":"Chen R. C.","year":"2020","journal-title":"Int. J. Appl. Sci. Eng."},{"key":"S2196888822500191BIB009","first-page":"1","volume-title":"Int. Conf. Learning Representations","author":"Radford A.","year":"2016"},{"key":"S2196888822500191BIB011","doi-asserted-by":"publisher","DOI":"10.3390\/app11072913"},{"key":"S2196888822500191BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"S2196888822500191BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"S2196888822500191BIB014","first-page":"12744","volume-title":"36th Int. Conf. Machine Learning, ICML 2019","author":"Zhang H.","year":"2019"},{"key":"S2196888822500191BIB015","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2714691"},{"key":"S2196888822500191BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/ICAwST.2019.8923404"},{"key":"S2196888822500191BIB017","first-page":"6997","volume":"10","author":"Tai S.","year":"2020","journal-title":"Appl. Sci. (Switzerland)"},{"key":"S2196888822500191BIB018","author":"Fang W.","year":"2019","journal-title":"IEEE Access"},{"key":"S2196888822500191BIB019","volume":"12","author":"Dewi C.","year":"2021","journal-title":"J. Ambient Intell. Humanized Comput."},{"key":"S2196888822500191BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814090"},{"key":"S2196888822500191BIB021","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73280-6_38"},{"key":"S2196888822500191BIB022","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2957453"},{"key":"S2196888822500191BIB023","first-page":"1","volume-title":"5th Int. Conf. Learning Representations, ICLR 2017 \u2014 Conf. Track Proc.","author":"Yang J.","year":"2019"},{"key":"S2196888822500191BIB024","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.004"},{"key":"S2196888822500191BIB025","first-page":"298","volume-title":"34th Int. Conf. Machine Learning, ICML 2017","author":"Arjovsky M.","year":"2017"},{"key":"S2196888822500191BIB026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ACCESS.2018.2876146","volume":"7","author":"Lu Y.","year":"2019","journal-title":"IEEE Access"},{"key":"S2196888822500191BIB028","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"S2196888822500191BIB029","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems","volume":"27","author":"Goodfellow I.","year":"2014"},{"key":"S2196888822500191BIB031","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9049005"},{"key":"S2196888822500191BIB032","doi-asserted-by":"publisher","DOI":"10.1109\/QRS-C.2019.00114"},{"key":"S2196888822500191BIB035","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29894-4_25"},{"key":"S2196888822500191BIB036","doi-asserted-by":"publisher","DOI":"10.5121\/csit.2019.91713"},{"key":"S2196888822500191BIB037","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"S2196888822500191BIB038","first-page":"2027","volume":"15","author":"Dewi C.","year":"2019","journal-title":"Int. J. Innov. Comput., Inf. Control"},{"key":"S2196888822500191BIB039","author":"Wang Z.","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"S2196888822500191BIB040","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2019.8913868"},{"key":"S2196888822500191BIB041","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-42058-1_24"},{"key":"S2196888822500191BIB042","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2014.2356501"},{"key":"S2196888822500191BIB043","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00327-4"},{"key":"S2196888822500191BIB044","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-14132-5_3"}],"container-title":["Vietnam Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S2196888822500191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T09:12:30Z","timestamp":1662628350000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S2196888822500191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,26]]},"references-count":38,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.1142\/S2196888822500191"],"URL":"https:\/\/doi.org\/10.1142\/s2196888822500191","relation":{},"ISSN":["2196-8888","2196-8896"],"issn-type":[{"value":"2196-8888","type":"print"},{"value":"2196-8896","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,26]]}}}