{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:10:22Z","timestamp":1760148622154,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002461","name":"Chungbuk National University","doi-asserted-by":"publisher","award":["BK21 Program (2022)."],"award-info":[{"award-number":["BK21 Program (2022)."]}],"id":[{"id":"10.13039\/501100002461","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automated inspection systems utilizing computer vision technology are effective in managing traffic control devices (TCDs); however, they face challenges due to the limited availability of training datasets and the difficulty in generating new datasets. To address this, our study establishes a benchmark for cost-effective model training methods that achieve the desired accuracy using data from related domains and YOLOv5, a one-stage object detector known for its high accuracy and speed. In this study, three model cases were developed using distinct training approaches: (1) training with COCO-based pre-trained weights, (2) training with pre-trained weights from the source domain, and (3) training with a synthesized dataset mixed with source and target domains. Upon comparing these model cases, this study found that directly applying source domain data to the target domain is unfeasible, and a small amount of target domain data is necessary for optimal performance. A model trained with fine-tuning-based domain adaptation using pre-trained weights from the source domain and minimal target data, proved to be the most resource-efficient approach. These results contribute valuable guidance for practitioners aiming to develop TCD models with limited data, enabling them to build optimal models while conserving resources.<\/jats:p>","DOI":"10.3390\/rs15102584","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:27:04Z","timestamp":1684204024000},"page":"2584","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions"],"prefix":"10.3390","volume":"15","author":[{"given":"Dahyun","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7293-2171","authenticated-orcid":false,"given":"Kyubyung","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Construction Management Technology, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Sungchul","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9440-6719","authenticated-orcid":false,"given":"Jinwu","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Construction Management Technology, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0897-7645","authenticated-orcid":false,"given":"Kyochul","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3957-9385","authenticated-orcid":false,"given":"Kibum","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Hyungkeun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1362-4096","authenticated-orcid":false,"given":"Jeonghun","family":"Won","sequence":"additional","affiliation":[{"name":"Department of Safety Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aei.2018.05.003","article-title":"Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach","volume":"37","author":"Fang","year":"2018","journal-title":"Adv. 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