{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:52:54Z","timestamp":1778500374901,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T00:00:00Z","timestamp":1654992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Land, Infrastructure and Transport","award":["22QPWO-C158103-03"],"award-info":[{"award-number":["22QPWO-C158103-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road segmentation has been one of the leading research areas in the realm of autonomous driving cars due to the possible benefits autonomous vehicles can offer. Significant reduction of crashes, greater independence for the people with disabilities, and reduced traffic congestion on the roads are some of the vivid examples of them. Considering the importance of self-driving cars, it is vital to develop models that can accurately segment drivable regions of roads. The recent advances in the area of deep learning have presented effective methods and techniques to tackle road segmentation tasks effectively. However, the results of most of them are not satisfactory for implementing them into practice. To tackle this issue, in this paper, we propose a novel model, dubbed as TA-Unet, that is able to produce quality drivable road region segmentation maps. The proposed model incorporates a triplet attention module into the encoding stage of the U-Net network to compute attention weights through the triplet branch structure. Additionally, to overcome the class-imbalance problem, we experiment on different loss functions, and confirm that using a mixed loss function leads to a boost in performance. To validate the performance and efficiency of the proposed method, we adopt the publicly available UAS dataset, and compare its results to the framework of the dataset and also to four state-of-the-art segmentation models. Extensive experiments demonstrate that the proposed TA-Unet outperforms baseline methods both in terms of pixel accuracy and mIoU, with 98.74% and 97.41%, respectively. Finally, the proposed method yields clearer segmentation maps on different sample sets compared to other baseline methods.<\/jats:p>","DOI":"10.3390\/s22124438","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["TA-Unet: Integrating Triplet Attention Module for Drivable Road Region Segmentation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-9439","authenticated-orcid":false,"given":"Sijia","family":"Li","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Furkat","family":"Sultonov","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Hainan University, Haikou 570100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Hainan University, Haikou 570100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chilsig","family":"Yang","sequence":"additional","affiliation":[{"name":"METROTECH Co., Ltd., Yeonam Bldg, 6, Yeongdong-daero 118-gil, Gangnam-gu, Seoul 06089, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minseok","family":"Song","sequence":"additional","affiliation":[{"name":"METROTECH Co., Ltd., Yeonam Bldg, 6, Yeongdong-daero 118-gil, Gangnam-gu, Seoul 06089, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungwoo","family":"Koo","sequence":"additional","affiliation":[{"name":"METROTECH Co., Ltd., Yeonam Bldg, 6, Yeongdong-daero 118-gil, Gangnam-gu, Seoul 06089, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8181-5994","authenticated-orcid":false,"given":"Jae-Mo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MITS.2014.2306552","article-title":"Making bertha drive\u2014An autonomous journey on a historic route","volume":"6","author":"Ziegler","year":"2014","journal-title":"IEEE Intell. 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