{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:22:48Z","timestamp":1768339368435,"version":"3.49.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Automatic road crack detection plays a major role in developing an intelligent transportation system. The traditional approach of in-situ inspection is expensive and requires more man-power. In-order to solve this problem, a novel approach for automatic road crack segmentation was developed using Stack Generative adversarial network Discriminator-U-Network (SGD-U-Network). We have collected 19 300 crack and non-crack images (MIT-CHN-ORR dataset) from the Outer Ring Road of Chennai, TamilNadu, India. The MIT-CHN-ORR dataset was initially pre-processed using traditional image processing techniques for ground truth image generation. A stage-I and stage-II stack Generative Adversarial Network (GAN) model was introduced for generating high-resolution non-crack images. Then, the extracted features from Stack GAN Discriminator of stage II (SGD2) was concatenated with every level of expansion path in SGD-U-Network for segmenting the crack regions of the input crack images. Also, multi-feature-based classifier was developed using the features extracted from SGD2 and the bottleneck layer of SGD-U-Network. Our proposed model was implemented on MIT-CHN-ORR dataset and also analyzed our model performance using other existing benchmark datasets. The experimental analysis showcased that the proposed method outperformed the other state-of-the-art approaches.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac029","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T12:11:52Z","timestamp":1646827912000},"page":"1595-1608","source":"Crossref","is-referenced-by-count":3,"title":["A Novel SGD-U-Network-Based Pixel-Level Road Crack Segmentation and Classification"],"prefix":"10.1093","volume":"66","author":[{"given":"Aravindkumar","family":"Sekar","sequence":"first","affiliation":[{"name":"Department of Computer Technology , Anna University - MIT Campus, Chennai 60044, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varalakshmi","family":"Perumal","sequence":"additional","affiliation":[{"name":"Department of Computer Technology , Anna University - MIT Campus, Chennai 60044, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"2023071709111593200_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/s19194251","article-title":"Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder-decoder network","volume":"19","author":"Islam","year":"2019","journal-title":"Sensors"},{"key":"2023071709111593200_ref2","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/TITS.2015.2477675","article-title":"Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection","volume":"17","author":"Amhaz","year":"2016","journal-title":"IEEE Trans. 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Neural Netw."},{"key":"2023071709111593200_ref27","first-page":"1","article-title":"Crackw-net: a novel pavement crack image segmentation convolutional neural network","volume":"1","author":"Han","year":"2021","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"2023071709111593200_ref28","doi-asserted-by":"crossref","first-page":"e2551","DOI":"10.1002\/stc.2551","article-title":"Cracku-net: a novel deep convolutional neural network for pixelwise pavement crack detection","volume":"27","author":"Huyan","year":"2020","journal-title":"Struct. Control. Health Monit."}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/7\/1595\/50876302\/bxac029.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/7\/1595\/50876302\/bxac029.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T09:17:02Z","timestamp":1689585422000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/66\/7\/1595\/6561441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":28,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,3,31]]},"published-print":{"date-parts":[[2023,7,13]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxac029","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,7]]},"published":{"date-parts":[[2022,3,31]]}}}