Target Detection Algorithm Based on FEW-YOLOv8 Remote Sensing Images

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Target Detection Algorithm Based on FEW-YOLOv8 Remote Sensing Images

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[1]XI Yangli,QU Dan,WANG Fangfang,et al.Target Detection Algorithm Based on FEW-YOLOv8 Remote Sensing Images[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):62-69.[doi:10.13705/j.issn.1671-6833.2025.04.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 04 Page number: 62-69 Column: Public date: 2025-07-10

Title:: Target Detection Algorithm Based on FEW-YOLOv8 Remote Sensing Images

Author(s):: XI Yangli¹; QU Dan²; 3; WANG Fangfang¹; DU Liming¹; 1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. School of Information System Engineering, Strategic Support Force Information Engineering University, Zhengzhou 450001, China; 3. Laboratory for Advanced Computing and Intelligent Engineering, Zhengzhou 450001, China

Keywords:: remote sensing images; YOLOv8; FasterNet backbone network; EMA attention mechanism; WIoU loss function

CLC:: TP389. 1

DOI:: 10.13705/j.issn.1671-6833.2025.04.007

Abstract:: Aiming at the problems of lack of small target information during feature extraction, partial loss of information during feature fusion, and inconspicuous small target feature information in remote sensing image target detection task, which lead to the low accuracy of small target detection, an algorithm for remote sensing image target detection based on FEW-YOLOv8 model was proposed. Firstly, the backbone network architecture was optimized to use the FasterNet backbone network, which extracted the spatial features of small targets in remote sensing images more efficiently, making the network model more focused on tiny targets, thus improving the small target detection accuracy. Secondly, the new C2f_EMA module was constructed using EMA attention and C2f to replace the C2f module in Neck network, and the feature attention enhancement operation was performed before fusing the features, so that the network model highlighted the small-target part of the feature information more, which effectively solved the problem of small-target feature loss in the process of feature fusion. Finally, WIoUv3, which had a dynamic non-monotonic FM, was used as the bounding box loss function to improve the accuracy of the model′s bounding box localization and strengthen the localization ability of small targets. The experimental results on NWPU VHR-10, HRSC2016 and DOTA v1. 0 datasets showed that the test mAP50 of the improved YOLOv8 algorithm was 7. 71, 9. 70 and 12. 32 percentage points higher than that of the original YOLOv8 algorithm, respectively, which proved that the proposed algorithm could effectively improve the detection accuracy of small targets in remote sensing images.

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Last Update: 2025-07-13