A Fine-Grained Aircraft Target Recognition Algorithm for Remote Sensing Images Based on YOLOV8

作     者：Jiang, Xiao-Nan Niu, Xiang-Qian Wu, Fan-Lu Fu, Yao Bao, He Fan, Yan-Chao Zhang, Yu Pei, Jun-Yan 

作者机构：Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Sch Optoelect Beijing 100049 Peoples R China 

出 版 物：《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》 (IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.)

年 卷 期：2025年第18卷

页      面：4060-4073页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：National Key R&D Program of China [2022YFB3902300] Natural Science Foundation of Jilin Province [20220101168JC] 

主　　题：Target recognition Remote sensing Feature extraction Aircraft Image recognition Accuracy Transformers Object detection Atmospheric modeling Semantics Feature fusion fine-grained recognition remote sensing images YOLOv8 

摘      要：Fine-grained recognition plays a pivotal role in the field of remote sensing image analysis, particularly in critical applications such as reconnaissance and early warning, intelligence analysis, and intelligent interpretation. However, the extensive coverage of remote sensing images, the low pixel ratio of targets, and the subtlety of features pose significant challenges for fine-grained recognition of aircraft targets. This article addresses the issues of missed and false detections in existing aircraft target fine-grained recognition algorithms for remote sensing images by proposing an improved algorithm based on YOLOv8, called FD-YOLOv8 (Focus Detail-YOLOv8). Initially, this article designs a local detail feature module to tackle the problem of information loss in shallow networks. This module enhances the capture of semantic information while extracting shallow features, thereby preserving more fine-grained features and improving the network s feature extraction capability. Subsequently, a focus modulation mechanism is employed to enhance the network s interactive understanding of local and global features, thereby improving the recognition accuracy for small and challenging targets. Finally, a multitype feature fusion is designed, which optimizes the generation of feature maps by integrating local features, high-level semantic information, and low-level texture information, enhancing the accuracy of fine-grained target recognition. Experiments conducted on the public remote sensing image dataset FAIR1M demonstrated that the YOLOv8n algorithm achieved a mean average precision (mAP) of 81.8% for aircraft category recognition tasks. In contrast, FD-YOLOv8 exhibited superior performance, with an mAP of 85.0%, indicating a significant advantage in fine-grained recognition.