{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:50:25Z","timestamp":1774965025091,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61873086"],"award-info":[{"award-number":["61873086"]}]},{"name":"National Natural Science Foundation of China","award":["BE2023340"],"award-info":[{"award-number":["BE2023340"]}]},{"name":"Jiangsu Province Key R&amp;D Program","award":["61873086"],"award-info":[{"award-number":["61873086"]}]},{"name":"Jiangsu Province Key R&amp;D Program","award":["BE2023340"],"award-info":[{"award-number":["BE2023340"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Small object detection for unmanned aerial vehicle (UAV) image scenarios is a challenging task in the computer vision field. Some problems should be further studied, such as the dense small objects and background noise in high-altitude aerial photography images. To address these issues, an enhanced YOLOv8s-based model for detecting small objects is presented. The proposed model incorporates a parallel multi-scale feature extraction module (PMSE), which enhances the feature extraction capability for small objects by generating adaptive weights with different receptive fields through parallel dilated convolution and deformable convolution, and integrating the generated weight information into shallow feature maps. Then, a scale compensation feature pyramid network (SCFPN) is designed to integrate the spatial feature information derived from the shallow neural network layers with the semantic data extracted from the higher layers of the network, thereby enhancing the network\u2019s capacity for representing features. Furthermore, the largest-object detection layer is removed from the original detection layers, and an ultra-small-object detection layer is applied, with the objective of improving the network\u2019s detection performance for small objects. Finally, the WIOU loss function is employed to balance high- and low-quality samples in the dataset. The results of the experiments conducted on the two public datasets illustrate that the proposed model can enhance the object detection accuracy in UAV image scenarios.<\/jats:p>","DOI":"10.3390\/rs16132465","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T11:46:05Z","timestamp":1720179965000},"page":"2465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["A Small-Object Detection Model Based on Improved YOLOv8s for UAV Image Scenarios"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7130-8331","authenticated-orcid":false,"given":"Jianjun","family":"Ni","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5719-5410","authenticated-orcid":false,"given":"Shengjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-6392","authenticated-orcid":false,"given":"Guangyi","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4829-7461","authenticated-orcid":false,"given":"Chunyan","family":"Ke","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8615-2034","authenticated-orcid":false,"given":"Tingting","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, G., Ni, J., Zhao, Y., Gu, Y., and Cao, W. 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