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- Title
基于注意力和自适应特征融合的 SAR 图像飞机目标检测.
- Authors
夏一帆; 赵凤军; 王樱洁; 王春乐
- Abstract
For the problems of multi-scale aircraft targets and strong scattering interference in the background in synthetic aperture radar(SAR) images,an improved YOLOv4 SAR target detection algorithm based on coordinate attention and adaptive feature fusion is proposed. Firstly,the algorithm introduces the coordinate attention mechanism in the backbone network to enhance the focusing ability on the combined structure of aircraft scattering features and the resistance to background interference. Secondly,the adaptive spatial feature fusion mechanism is implemented in the feature enhancement network to improve the feature extraction capability for aircraft of different sizes,while improving the imbalance between the recall and accuracy rates of YOLOv4. Finally,the size of the prior anchor is adjusted for aircraft targets by improved K-means clustering to improve the localization accuracy of the model. The experimental results show that the improved YOLOv4 achieves 91. 01% recall, 90. 09% accuracy, and 92. 34% AP0.5, which are 2. 49%,6. 56%,and 3. 62% better than YOLOv4 respectively.
- Publication
Telecommunication Engineering, 2024, Vol 64, Issue 3, p350
- ISSN
1001-893X
- Publication type
Article
- DOI
10.20079/j.issn.1001-893x.221014002