Total views : 560
Ship Detection in Images Obtained from the Unmanned Aerial Vehicle (UAV)
Background/Objectives: The main objective of this work is the creation of a new algorithm for detecting ships in the high resolution images obtained by unmanned aerial vehicle. Method: To achieve the objective the developed ship detection algorithm uses two cascades: shape-based cascade and cascade based on automatically extracted features. Deep neural network autoencoder is applied for automatic feature extraction. Findings: Images from Google Maps, Yandex Maps, and a small fraction of the images taken from the Internet were used for training and testing the developed approach. In total, the dataset consists of 1000 images containing ships. On average, each image contains 5.1 ships. The dataset was divided into training (50%) and test (50%). Detection quality was estimated using the Recall and Precision metrics. Our study has shown that the combined use of shape-based cascade and cascade based on automatically extracted features allows achieving high quality (Recall: 0.95, Precision: 0.94) and performance in the problem of ship detection by UAV. The results demonstrate that the proposed method is comparable or slightly outperforms the state-of-the-art methods. Improvements/Applications: Applying the detector in real video from the UAV, it would be possible to improve performance by parallelization of this approach on the GPU.
Connected Components, Deep Autoencoders, Naive-Bayes Classifier, Ship Detection, UAV.
- Wang C, Liao M, Li X. Ship detection in SAR Image Based on the Alpha-stable Distribution. Sensors. 2008; 8(8):4948– 60. DOI:10.3390/s8084948.
- Xiangwei X, Kefeng J, Huanxin Z, Jixiang S. A fast ship detection algorithm in SAR imagery for wide area ocean surveillance. Proceedings of 2012 IEEE Radar Conference (RADAR); 2012. p. 0570–0574. DOI: 10.1109/ RADAR.2012.6212205.
- Xing X, Ji K, Kang L, Zhan M. Review of ship surveillance technologies based on high-resolution wide-swath synthetic aperture radar imaging. Journal of Radars. 2015; 4(1):107–21. DOI: 10.12000/JR14144.
- Zhang R, Yao J, Zhang K, Feng C, Zhang J. S-CNN-based ship detection from high-resolution remote sensing images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, XXIII ISPRS Congress, 2016 12–19 Jul; 2016. p. 423–30.DOI:10.5194/isprsarchives-XLI-B7-423-2016.
- Shi Z, Yu X, Jiang Z, Li B. Ship detection in high resolution optical imagery based on anomaly detector and local shape feature. IEEE Transactions on Geoscience and Remote Sensing. 2014, 52(8):4511–23. DOI: 10.1109/ TGRS.2013.2282355.
- Haigang S, Zhina S. A novel ship detection method for large-scale optical satellite images based on visual LBP feature and visual attention model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress 2016, XLI-B3; 2016. p. 917–21. DOI:10.5194/isprs-archives-XLIB3917-2016.
- Xu C, Zhang D, Zhang Z, Feng Z. BgCut: Automatic ship detection from UAV images. Hindawi Publishing Corporation. The Scientific World Journal. 2014; (2014):1– 11. DOI:10.1155/2014/171978.
- Zou Z, Shi Z. Ship detection in spaceborne optical image with SVD networks. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54(10):5832–45. DOI: 10.1109/ TGRS.2016.2572736.
- Qi S, Ma J, Lin J, Li Y, Tian J. Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images. IEEE Geoscience and Remote Sensing Letters.2015, 12(7):1451–5. DOI: 10.1109/LGRS.2015.2408355.
- Feineigle PA, Morris DD, Snyder FD. Ship recognition using optical imagery for harbor surveillance. Proceedings of Association for Unmanned Vehicle Systems International (AUVSI); 2007 Jun. p. 1–17.
- Dalal N, Triggs B. Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2005; 1:886–93. DOI: 10.1109/CVPR.2005.177
- Zeng C, Ma H. Robust head-shoulder detection by PCA based multilevel hog-lbp detector for people counting.Proceedings of 20th International Conference on Pattern Recognition (ICPR); 2010. p. 2069–72. DOI: 10.1109/ ICPR.2010.509.
- Shi Z, Yu X, Jiang Z, Li B. Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature. IEEE Transactions on Geoscience and Remote Sensing 2014; 52(8):4511–23. DOI: 10.1109/ TGRS.2013.2282355.
- Morillas JRA, Garcia IC, Zolzer U. Ship detection based on SVM using color and texture features. 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca; 2015. p. 343–50. DOI: 10.1109/ICCP.2015.7312682.
- Eum H, Bae J, Yoon C, Kim E. Ship detection using edgebased segmentation and histogram of oriented gradient with ship size ratio. International Journal of Fuzzy Logic and Intelligent Systems. 2015; 15(4):251–9, http://dx.doi.org/10.5391/IJFIS.2015.15.4.251.
- Tang J, Deng C, Huang G-B, Zhao B. Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Transactions on Geoscience and Remote Sensing. 2015; 53(3):1174–85. DOI:10.1109/TGRS.2014.2335751.
- Othman E, Bazi Y, Alajlan N, Alhichri H, Melgani F. Using convolutional features and a sparse autoencoder for land-use scene classification. International Journal of Remote Sensing. 2016; 37(10):1977–95. DOI: 10.1080/01431161.2016.1171928
- Google maps [Internet]. [cited 2016 Aug 21]. Available from: https://www.google.ru/maps.
- Yandex maps [Internet]. [cited 2016 Aug 21]. Available from: https://yandex.ru/maps/.
- Skribtsov P, Kazantsev P, Dolgopolov A. Head-shoulder detection using deep autoencoders. Indian Journal of Science and Technology. 2016; 9(42). DOI: 10.17485/ ijst/2016/v9i42/104302.
- Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to algorithms. (3d ed.). MIT Press and McGraw-Hill; 2009.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.