Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks
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 pr...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/4685644 |
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| _version_ | 1832546034753994752 |
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| author | Liming Zhou Haoxin Yan Yingzi Shan Chang Zheng Yang Liu Xianyu Zuo Baojun Qiao |
| author_facet | Liming Zhou Haoxin Yan Yingzi Shan Chang Zheng Yang Liu Xianyu Zuo Baojun Qiao |
| author_sort | Liming Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fc6072b12e5a4cdbac278cc77c68134b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-fc6072b12e5a4cdbac278cc77c68134b2025-02-03T07:24:03ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/46856444685644Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural NetworksLiming Zhou0Haoxin Yan1Yingzi Shan2Chang Zheng3Yang Liu4Xianyu Zuo5Baojun Qiao6Henan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaYellow River Conservancy Technical Institute, Kaifeng 475000, ChinaHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng 475000, ChinaAircraft 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.http://dx.doi.org/10.1155/2021/4685644 |
| spellingShingle | Liming Zhou Haoxin Yan Yingzi Shan Chang Zheng Yang Liu Xianyu Zuo Baojun Qiao Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks Journal of Electrical and Computer Engineering |
| title | Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks |
| title_full | Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks |
| title_fullStr | Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks |
| title_full_unstemmed | Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks |
| title_short | Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks |
| title_sort | aircraft detection for remote sensing images based on deep convolutional neural networks |
| url | http://dx.doi.org/10.1155/2021/4685644 |
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