{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:58:16Z","timestamp":1769043496919,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.<\/jats:p>","DOI":"10.3390\/e24010077","type":"journal-article","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T22:51:50Z","timestamp":1641250310000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Domain-Specific On-Device Object Detection Method"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8306-2028","authenticated-orcid":false,"given":"Seongju","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"given":"Jaegi","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"given":"Kwangsue","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102897","DOI":"10.1016\/j.cviu.2019.102897","article-title":"Monocular human pose estimation: A survey of deep learning-based methods","volume":"192","author":"Chen","year":"2020","journal-title":"Comput. 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