{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:36:20Z","timestamp":1767108980467,"version":"3.37.3"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T00:00:00Z","timestamp":1640563200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of State Administration for Market Regulation","award":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"],"award-info":[{"award-number":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"],"award-info":[{"award-number":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Central Foundational Research Funding Project","award":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"],"award-info":[{"award-number":["2020 MK 162","61771471","61401463","U1613213,","91748131","562020Y-7482"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,12,27]]},"abstract":"<jats:p>Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance, and night vision. Deep learning-based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN-based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain, and then the off-the-shelf domain adaptive faster RCNN is utilized to reduce the gap between the generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method.<\/jats:p>","DOI":"10.1155\/2021\/1837894","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T18:50:28Z","timestamp":1640631028000},"page":"1-6","source":"Crossref","is-referenced-by-count":17,"title":["Unsupervised Image-Generation Enhanced Adaptation for Object Detection in Thermal Images"],"prefix":"10.1155","volume":"2021","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"China National Institute of Standardization, Beijing, China"}]},{"given":"Fuyu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shanshan","family":"Yuan","sequence":"additional","affiliation":[{"name":"China National Institute of Standardization, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-7132","authenticated-orcid":true,"given":"Wanyi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1007\/s00138-013-0570-5"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1109\/tpami.2016.2577031"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1007\/978-3-319-46448-0_2"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1109\/cvpr.2016.91"},{"author":"C. Herrmann","article-title":"CNN-based thermal infrared person detection by domain adaptation","key":"5"},{"doi-asserted-by":"publisher","key":"6","DOI":"10.1109\/icip.2019.8803104"},{"doi-asserted-by":"publisher","key":"7","DOI":"10.1109\/cvpr.2018.00352"},{"author":"W. Li","first-page":"1808","article-title":"Deep domain adaptive object detection: a survey","key":"8"},{"doi-asserted-by":"publisher","key":"9","DOI":"10.1109\/iccv.2017.244"},{"doi-asserted-by":"publisher","key":"10","DOI":"10.1145\/3126686.3126727"},{"author":"A. Paszke","first-page":"8024","article-title":"PyTorch: an imperative style, high-performance deep learning library","key":"11"},{"key":"12"},{"key":"13"},{"year":"2021","author":"P. Liu","article-title":"Unsupervised image-generation enhanced adaptation for object detection in thermal images","key":"14"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1837894.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1837894.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2021\/1837894.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T18:50:37Z","timestamp":1640631037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2021\/1837894\/"}},"subtitle":[],"editor":[{"given":"Hye-jin","family":"Kim","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,12,27]]},"references-count":14,"alternative-id":["1837894","1837894"],"URL":"https:\/\/doi.org\/10.1155\/2021\/1837894","relation":{},"ISSN":["1875-905X","1574-017X"],"issn-type":[{"type":"electronic","value":"1875-905X"},{"type":"print","value":"1574-017X"}],"subject":[],"published":{"date-parts":[[2021,12,27]]}}}