Fast aircraft detection method in optical remote sensing images based on deep learning

Xu, Zhi-Feng; Jia, Rui-Sheng; Yu, Juan; Sun, Hong-Mei

doi:10.1117/1.jrs.15.014502

Cited by 6 publications

(2 citation statements)

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“…Wu et al [17] aimed at the problem that aircraft targets were usually small and the cost of manual annotation was very high; a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL) was proposed to know detectors with only image-level annotations. Xu et al [18] aimed at the problem that the aircraft to be detected was very small in optical remote sensing images and the interference of objects to the aircraft had a great impact on the aircraft characteristics in remote sensing images; a multiscale fusion prediction network (MFPN) was proposed to perform feature fusion from multiple angles to achieve a rich combination of gradients. Wu et al [19] aimed to enhance the detection effect in the high-resolution remote sensing images which contained the dense targets and complex background; an improved Mask R-CNN model, called SCMask R-CNN, was proposed.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Zhou

Yan

Shan

et al. 2021

Journal of Electrical and Computer Engineering

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Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading to detecting difficulty. The other reason is that the background of the remote sensing image is usually complex, so the algorithms applied to the scenario are easy to be affected by the background. To address the problem of small size, this paper proposes the Multiscale Detection Network (MSDN) which introduces a multiscale detection architecture to detect small-scale aircrafts. With the intention to resist the background noise, this paper proposes the Deeper and Wider Module (DAWM) which increases the perceptual field of the network to alleviate the affection. Besides, to address the two problems simultaneously, this paper introduces the DAWM into the MSDN and names the novel network structure as Multiscale Refined Detection Network (MSRDN). The experimental results show that the MSRDN method has detected the small-scale aircrafts that other algorithms missed and the performance indicators have higher performance than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Zhou

Yan

Shan

et al. 2021

Journal of Electrical and Computer Engineering

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“…Shi et al [ 24 ] aimed at the problem that it was still a challenge in remote sensing detection due to complex background and multiscale characteristics, proposing a two-stage aircraft detection method based on deep neural networks, which integrated Deconvolution operation with Position Attention mechanism (DPANet). Xu et al [ 25 ] aimed at the problem that the aircraft to be detected was very small, external environmental factors were easily fused, and the interference of objects to aircraft had a great impact on the aircraft characteristics in remote sensing images, proposing a remote sensing aircraft detection method based on deep learning. Zhou et al [ 26 ] aimed at the problem that the recent algorithms would miss some small-scale aircrafts when applied to the remote sensing image, proposing the Multiscale Detection Network (MSDN), which introduced a multiscale detection architecture to detect small-scale aircrafts.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

Zhou

Yan

Zheng

et al. 2021

Computational Intelligence and Neuroscience

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Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

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Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

Zeng

Ming

et al. 2022

International Journal of Remote Sensing

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Fast aircraft detection method in optical remote sensing images based on deep learning

Cited by 6 publications

References 0 publications

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

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