ABSTRACT
Military aircraft detection is always a challenging problem in the development of defence technology since World War II (WWII). The target is usually captured by electromagnetic wave reflection, which is accomplished by radar and CCD camera. The existed studies in this field mainly focused on aircraft detection in airbase or airports so far. It is noteworthy that the aircraft carrier is also an essential vector for the military aircraft, which is more challenging for aircraft detection, especially in the complex sea condition. This paper proposed a novel method of carrier-based aircraft detection on the flight deck of an aircraft carrier with simulated 3-D model. We construct a Parallel Convolutional Neural Network (PCNN) to train the simulated 3-D model dataset, which is also used for the final detection process. Finally, we apply the generalized deep convolutional neural network to detect the real reconnaissance images of the aircraft carrier with carrier-based aircraft on the flight deck. Experiments result show that the proposed method achieves an average detection accuracy of 99.92% in the real reconnaissance images, which contributes to the surveillance and early warning of the actual naval warfare.
- Hui Wu; Hui Zhang; Jinfang Zhang; Fanjiang Xu. Fast aircraft detection in satellite images based on convolutional neural networks. 2015 IEEE International Conference on Image Processing. Quebec City, QC, Canada, 27-30 Sept. 2015Google Scholar
- Wenhui Diao; Fangzheng Dou; Kun Fu; Xian Sun; Aircraft Detection in Sar Images Using Saliency Based Location Regression Network. IGARSS 2018 -2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain, 22-27 July 2018Google Scholar
- Jinsheng Ji; Tao Zhang; Zhen Yang; Linfeng Jiang, Aircraft Detection from Remote Sensing Image Based on A Weakly Supervised Attention Model, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July-2 Aug. 2019Google Scholar
- Pengfei Zhao; Huayu Gao; Yun Zhang; Hongbo Li; Rui Yang, An Aircraft Detection Method Based on Improved Mask R-CNN in Remotely Sensed Imagery, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July-2 Aug. 2019Google Scholar
- Bowen Cai; Zhiguo Jiang; Haopeng Zhang. Online Exemplar-Based Fully Convolutional Network for Aircraft Detection in Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2018, 15, 1095--1099Google ScholarCross Ref
- Jasmin James; Jason J. Ford; Timothy L. Molloy, "Quickest Detection of Intermittent Signals with Application to Vision-Based Aircraft Detection", IEEE Transactions on Control Systems Technology, Volume: 27, Issue: 6, 2019Google Scholar
- Yikui Zhai; Hui Ma; He Cao; Wenbo Deng; Jian Liu; Zhongyi Zhang; "MF-SarNet: Effective CNN with data augmentation for SAR automatic target recognition" The Journal of Engineering, Volume: 2019, Issue: 19, 10 2019, pp. 5813--5818Google Scholar
- Junmin Zhang; Yubo Zhang; Yonggang Guan, "Analysis of Time-Domain Reflectometry Combined with Wavelet Transform for Fault Detection in Aircraft Shielded Cables", IEEE Sensors Journal, vol. 16, issue. 11, 2016Google Scholar
- An Zhao; Kun Fu; Siyue Wang; Jiawei Zuo; Yuhang Zhang, "Aircraft Recognition Based on Landmark Detection in Remote Sensing Images" IEEE Geoscience and Remote Sensing Letters, Volume: 14, Issue: 8, 2017Google Scholar
- Feng Zhou; Li Wang; Xueru Bai, "SAR ATR of Ground Vehicles Based on LM-BN-CNN," IEEE Transactions on Geoscience and Remote Sensing, vol.56, no.12, pp.7282--7293, Dec. 2018Google ScholarCross Ref
- A Zhao; Kun Fu; Hao Sun; Xian Sun; Feng Li, "An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images", IEEE Geoscience and Remote Sensing Letters, vol. 14, Issue. 5, 2017Google Scholar
- K. Allweins; G. Gierelt; H.-J. Krause; Mv. Kreutzbruck, "Defect detection in thick aircraft samples based on HTS SQUID-magnetometry and pattern recognition", IEEE Transactions on Applied Superconductivity, Vol. 13, Issue. 2, 2003Google Scholar
- Junichi Honda; Takuya Otsuyama; Feasibility Study on Aircraft Positioning by Using ISDB-T Signal Delay; IEEE Antennas and Wireless Propagation Letters; Volume: 15, Issue: 7, 2016Google Scholar
- Ting Li; Guobin Yang; Pengxun Wang; Gang Chen; Chen Zhou; High-frequency radar aircraft detection method based on neural networks and time-frequency algorithm; IET Radar, Sonar & Navigation; Volume: 7, Issue: 8, 2013Google Scholar
- Jérémie Jakubowicz; Sidonie Lefebvre; Florian Maire; Eric Moulines; Detecting Aircraft with a Low-Resolution Infrared Sensor. IEEE Transactions on Image Processing, Volume: 21, Issue: 6, 2012Google Scholar
- Florian Maire; Sidonie Lefebvre; Detecting Aircraft in Low-Resolution Multispectral Images: Specification of Relevant IR Wavelength Bands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Volume: 8, Issue: 9, 2015Google Scholar
- Shu-zhu Shi; Zheng-yu Zhao; Yan Liu; Experimental Demonstration for Ionospheric Sensing and Aircraft Detection with a HF Skywave Multistatic Radar; IEEE Geoscience and Remote Sensing Letters; Volume: 11, Issue: 7, 2014Google Scholar
- H. Zhu, W. Wang and R. Leung, "SAR Target Classification Based on Radar Image Luminance Analysis by Deep Learning," in IEEE Sensors Letters, vol. 4, no. 3, pp. 1--4, March 2020, Art no. 7000804, doi: 10.1109/LSENS.2020.2976836.Google ScholarCross Ref
- H. Zhu, N. Lin, H. Leung, R. Leung and S. Theodoidis, "Target Classification From SAR Imagery Based on the Pixel Grayscale Decline by Graph Convolutional Neural Network," in IEEE Sensors Letters, vol. 4, no. 6, pp. 1--4, June 2020, Art no. 7002204, doi: 10.1109/LSENS.2020.2995060.Google ScholarCross Ref
Index Terms
- Carrier-based Aircraft Detection on Flight Deck of Aircraft Carrier with Simulated 3-D Model by Deep Neural Network
Comments