{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T12:33:10Z","timestamp":1793363590770,"version":"4.5.0"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The advancement of technology has ushered in remote sensing with the adoption of high-altitude infrared thermal object detection to leverage the distinct advantages of high-altitude platforms. These new technologies readily capture the thermal signatures of objects from an elevated point, generally unmanned aerial vehicles or drones, and thus allow for the enhancement of the detection and monitoring of extensive areas. This study explores the application of YOLOv8\u2019s advanced architecture, as well as dynamic magnitude-based pruning techniques paired with non-maximum suppression for high-altitude infrared thermal object detection using UAVs. The current research addresses the complexities of processing high-resolution thermal imagery, where traditional methods fall short. We converted dataset annotations from the COCO and PASCAL VOC formats to YOLO\u2019s required format, enabling efficient model training and inference. The results demonstrate the proposed architecture\u2019s superior speed and accuracy, effectively handling thermal signatures and object detection. Precision\u2013recall metrics indicate robust performance, though some misclassification, particularly for persons, suggests areas for further refinement. This work highlights the advanced architecture of YOLOv8\u2019s potential in enhancing UAV-based thermal imaging applications, paving the way for more effective real-time object detection solutions.<\/jats:p>","DOI":"10.3390\/jimaging11030069","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T10:52:47Z","timestamp":1740394367000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving Object Detection in High-Altitude Infrared Thermal Images Using Magnitude-Based Pruning and Non-Maximum Suppression"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3561-9597","authenticated-orcid":false,"given":"Yajnaseni","family":"Dash","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Bennett University, Greater Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6609-1207","authenticated-orcid":false,"given":"Vinayak","family":"Gupta","sequence":"additional","affiliation":[{"name":"School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Sai University, Chennai 603104, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swati","family":"Chandna","sequence":"additional","affiliation":[{"name":"Department of Information, Applied Data Science and Analytics, SRH University of Applied Sciences, 69123 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.knosys.2017.07.032","article-title":"Deep convolutional neural networks for thermal infrared object tracking","volume":"134","author":"Liu","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TMM.2020.3008028","article-title":"Learning deep multi-level similarity for thermal infrared object tracking","volume":"23","author":"Liu","year":"2020","journal-title":"IEEE Trans. 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