A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Author:

Chen XinORCID,Liu Jinghong,Xu Fang,Xie Zhihua,Zuo Yujia,Cao Lihua

Abstract

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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2. An improved SSD lightweight network with coordinate attention for aircraft target recognition in scene videos;Journal of Intelligent & Fuzzy Systems;2024-01-10

3. A benchmark dataset for deep learning-based airplane detection: HRPlanes;International Journal of Engineering and Geosciences;2023-10-15

4. Real Time Web-based System to Detect Military Aircraft Using RESNET-50 Algorithm;Electrical and Automation Engineering;2023-04-01

5. Small Object Detection Methods in Complex Background: An Overview;International Journal of Pattern Recognition and Artificial Intelligence;2023-01-28

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