{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:17:17Z","timestamp":1778285837792,"version":"3.51.4"},"reference-count":38,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":323,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The detection of small targets, particularly people and various types of vehicles, has long been recognised as a formidable and highly challenging task in remote sensing images. To improve the performance of small target detection, we propose IE\u2010YOLO, an information\u2010enhanced small target detection model based on YOLOv8. First, to enhance the model's ability to distinguish between the target and the background, or between the target and similar categories, the C2f module in the backbone is replaced with a custom\u2010designed vssC2f module. Second, to further amplify the features of small targets, a Feature Enhancement (FEB) Block is proposed to significantly strengthen overall feature extraction capability, enabling it to capture small target characteristics more effectively. Finally, an Information\u2010Enhanced Feature Pyramid Network (IE\u2010FPN) is proposed to fully utilise shallow features for retaining more small target information, significantly improving detection performance. Extensive experiments on the VisDrone2019 dataset show that the IE\u2010YOLO model achieves higher detection accuracy for small target detection than other advanced detection models. Compared with YOLOv8, the values of the mAP0.5 and the mAP0.5:0.95 have increased by 6.8% and 4.7%, respectively. It demonstrates significant application potential and practical value in the field of small target detection.<\/jats:p>","DOI":"10.1049\/ipr2.70250","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:12:12Z","timestamp":1763640732000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["IE\u2010YOLO: An Information\u2010Enhanced Small Target Detection Model for Remote Sensing Images"],"prefix":"10.1049","volume":"19","author":[{"given":"Haijiao","family":"Yun","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering Changchun University  Changchun Jilin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqing","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering Changchun University  Changchun Jilin China"},{"name":"School of the Graduate Changchun University  Changchun Jilin 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