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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1567-1571

Small object detection algorithm on UAV aerial images based on enhanced lower feature

Lyu Xiaojun1,2,3,4,5
Xiang Wei1,2,4,5
Liu Yunpeng1,2,4,5
1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Key Laboratory of Opto-electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5. Key Laboratory of Image Understanding & Computer Vision, Shenyang 110016, China
DOI: 10.19734/j.issn.1001-3695.2020.04.0148

Abstract

In order to solve the problem of low accuracy and residual error in small object detection on UAV aerial images, this paper proposed a new kind of multi-scale small target detection method based on enhanced lower feature. Basing on Faster R-CNN ResNet-50-FPN model, the algorithm enhanced the lower feature by designing the structure of DetNet-59 feature extraction network and Flat-FPN feature fusion network, and applied soft-NMS to face the appearance of overlapping small objects. From simulation test on VOC2007 and VisDrone2019, the method is able to increase mAP by 11% compared to the base model when time consumption is no more than 2%, and it also performs better in terms of accuracy than current common algorithms. It was proved that the algorithm can effectively improve the detection accuracy of small targets while ensuring real-time performance.

Foundation Support

中国科学院科技创新重点基金资助项目(Y8K4160401)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0148
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Technology of Graphic & Image
Pages: 1567-1571
Serial Number: 1001-3695(2021)05-054-1567-05

Publish History

[2021-05-05] Printed Article

Cite This Article

吕晓君, 向伟, 刘云鹏. 基于强化底层特征的无人机航拍图像小目标检测算法 [J]. 计算机应用研究, 2021, 38 (5): 1567-1571. (Lyu Xiaojun, Xiang Wei, Liu Yunpeng. Small object detection algorithm on UAV aerial images based on enhanced lower feature [J]. Application Research of Computers, 2021, 38 (5): 1567-1571. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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