Fast Remote Sensing Image Object Detection Algorithm Based on Attention Feature Fusion

被引：0

作者：

Wu, Jiancheng ^{[1
]}

Guo, Rongzuo ^{[1
]}

Cheng, Jiawei ^{[1
]}

Zhang, Hao ^{[1
]}

机构：

[1] College of Computer Science, Sichuan Normal University, Chengdu

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 01期

关键词：

attention mechanism; feature pyramid; object detection; remote sensing image; YOLO;

D O I：

10.3778/j.issn.1002-8331.2303-0375

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at the challenges of complex backgrounds, numerous small targets, and difficulty in feature extraction in remote sensing images, a fast remote sensing image object detection algorithm based on attention feature fusion—YOLO-Aff is proposed. This algorithm designs a backbone network module (ECALAN) with channel attention and a blur pool (BP) module to reduce the loss caused by downsampling. In addition, a feature pyramid network (SPD-FPN) with no stride convolution is used to combine the SimAM attention feature fusion module (CBSA) to enhance the cross-scale feature fusion performance of the features. Finally, Wise-IoU is used as the coordinate loss of the network to optimize the sample imbalance problem. The experimental results show that YOLO-Aff achieves an mAP value of 96% on the NWPU VHR-10 dataset, which is 2.9 percentage points higher than the original algorithm, and provides a new solution for fast and high-precision object detection of remote sensing images. © 2019 Remedium Group Ltd. All rights reserved.

引用

页码：207 / 216

页数：9

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