Depth-Reshaping Based Aerial Object Detection Enhanced Network

被引：0
作者：
Fu, Tianyi [1 ,2 ]
Yang, Benyi [3 ,4 ]
Dong, Hongbin [1 ,2 ]
Deng, Baosong [3 ,4 ]
机构：
[1] College of Computer Science and Technology, Harbin Engineering University, Harbin
[2] National Engineering Laboratory for Modeling and Emulation in E-Government, Harbin Engineering University, Harbin
[3] Defense Innovation Institute(DII), Academy of Military Science, Beijing
[4] Intelligent Game and Decision Laboratory, Academy of Military Science, Beijing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2024年 / 37卷 / 07期
基金：
黑龙江省自然科学基金; 中国国家自然科学基金;
关键词：
Aerial Image; Computer Vision; Deep Learning; Feature Extraction; Object Detection;
D O I：
10.16451/j.cnki.issn1003-6059.202407007
中图分类号：
学科分类号：
摘要：
To address the issues of complex background interference, loss of fine details in small objects and the high demand for detection efficiency in aerial image object detection, a depth-reshaping enhanced network(DR-ENet) is proposed. Firstly, the traditional downsampling methods are replaced by spatial depth-reshaping techniques to reduce information loss during feature extraction and enhance the ability of the network to capture details. Then, a deformable spatial pyramid pooling method is designed to enhance the adaptability of network to object shape variations and its ability to recognize in complex backgrounds. Simultaneously, an attention decoupling detection head is proposed to enhance the learning effectiveness for different detection tasks. Finally, a small-scale aerial dataset, PORT, is constructed to simultaneously consider the characteristics of dense small objects and complex backgrounds. Experiments on three public aerial datasets and PORT dataset demonstrate that DR-ENet achieves performance improvement, proving its effectiveness and high efficiency in aerial image object detection. © 2024 Science Press. All rights reserved.
引用
收藏
页码：652 / 662
页数：10
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