{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:11Z","timestamp":1760144291126,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RIS","award":["RIS-004","(P0024177, Development of RIC (Regional Innovation Cluster)"],"award-info":[{"award-number":["RIS-004","(P0024177, Development of RIC (Regional Innovation Cluster)"]}]},{"name":"Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government (MOTIE)","award":["RIS-004","(P0024177, Development of RIC (Regional Innovation Cluster)"],"award-info":[{"award-number":["RIS-004","(P0024177, Development of RIC (Regional Innovation Cluster)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.<\/jats:p>","DOI":"10.3390\/s24072371","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T09:31:23Z","timestamp":1712568683000},"page":"2371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles"],"prefix":"10.3390","volume":"24","author":[{"given":"Hwapyeong","family":"Baek","sequence":"first","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seunghyun","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seungwook","family":"Son","sequence":"additional","affiliation":[{"name":"Info Valley Korea Co., Ltd., Anyang 14067, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongwoong","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Calicioglu, O., Flammini, A., Bracco, S., Bell\u00f9, L., and Sims, R. 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