Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-07-04T19:48:29.809Z Has data issue: false hasContentIssue false
  • English
  • Français

Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

Published online by Cambridge University Press:  09 July 2021

Xinqiang Chen Open the ORCID record for Xinqiang Chen [Opens in a new window] ,
Jun Ling ,
Shengzheng Wang ,
Yongsheng Yang ,
Lijuan Luo  and
Ying Yan
Xinqiang Chen
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Jun Ling
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Shengzheng Wang
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, PR China
Yongsheng Yang
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Lijuan Luo*
Affiliation:
AI and Data Science Application Center, School of Business and Management, Shanghai International Studies University, Shanghai, China
Ying Yan
Affiliation:
College of Transportation, Chang'an University, Xi'an, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

Keywords

ship detectionensemble Canny-Gaussian-morphology frameworkcoastal surveillance videosmart ship
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1252 - 1266
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Biondi, F. (2017). Low-rank plus sparse decomposition and localized radon transform for ship-wake detection in synthetic aperture radar images. IEEE Geoscience and Remote Sensing Letters, 15, 117–121.CrossRefGoogle Scholar
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–698.CrossRefGoogle ScholarPubMed
Chen, Z. and Ellis, T. (2014). A self-adaptive Gaussian mixture model. Computer Vision & Image Understanding, 122, 35–46.CrossRefGoogle Scholar
Chen, X., Wang, S., Shi, C., Wu, H., Zhao, J. and Fu, J. (2019). Robust ship tracking via multi-view learning and sparse representation. Journal of Navigation, 72, 176–192.CrossRefGoogle Scholar
Chen, X., Qi, L., Yang, Y., Luo, Q., Postolache, O., Tang, J. and Wu, H. (2020a). Video-based detection infrastructure enhancement for automated ship recognition and behavior analysis. Journal of Advanced Transportation, 2020, 1–12.Google Scholar
Chen, X., Yang, Y., Wang, S., Wu, H., Tang, J., Zhao, J. and Wang, Z. (2020b). Ship type recognition via a coarse-to-fine cascaded convolution neural network. Journal of Navigation, 73, 813–832.10.1017/S0373463319000900CrossRefGoogle Scholar
Chen, Z., Chen, D., Zhang, Y., Cheng, X., Zhang, M. and Wu, C. (2020c). Deep learning for autonomous ship-oriented small ship detection. Safety Science, 130, 104812.10.1016/j.ssci.2020.104812CrossRefGoogle Scholar
Chen, X., Li, Z., Yang, Y., Qi, L. and Ke, R. (2021). High-resolution vehicle trajectory extraction and denoising from aerial videos. IEEE Transactions on Intelligent Transportation Systems, 22, 3190–3202.CrossRefGoogle Scholar
Dasari, M. M. and Gorthi, R. K. S. S. (2020). IOU – Siamtrack: IOU Guided Siamese Network for Visual Object Tracking. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP).CrossRefGoogle Scholar
Deng, G. and Cahill, L. W. (1993) An adaptive Gaussian filter for noise reduction and edge detection. Proceedings of the Nuclear Science Symposium & Medical Imaging Conference.Google Scholar
Deng, C.-X., Wang, G.-B. and Yang, X.-R. (2013) Image edge detection algorithm based on improved Canny operator. Proceedings of the 2013 International Conference on Wavelet Analysis and Pattern Recognition.Google Scholar
Graziano, M. D. (2020). Preliminary results of ship detection technique by wake pattern recognition in SAR images. Remote Sensing, 12, 2869.CrossRefGoogle Scholar
Harvey, N. R., Porter, R. and Theiler, J. (2010). Ship detection in satellite imagery using rank-order grayscale hit-or-miss transforms. Proceedings of the International Society for Optical Engineering, 6 April, Orlando, FL.10.1117/12.850886CrossRefGoogle Scholar
Huang, Y., Li, Y., Zhang, Z. and Liu, R. W. (2020). GPU-Accelerated Compression and visualization of large-scale vessel trajectories in maritime IoT industries. IEEE Internet of Things Journal, 7, 10794–10812.CrossRefGoogle Scholar
Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z. and Wang, Y. (2017). Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Transactions on Intelligent Transportation Systems, 18, 890–901.CrossRefGoogle Scholar
Kim, K., Hong, S., Choi, B. and Kim, E. (2018). Probabilistic ship detection and classification using deep learning. Applied Sciences, 8, 936.CrossRefGoogle Scholar
Le Caillec, J., Gorski, T., Sicot, G. and Kawalec, A. (2018). Theoretical performance of space-time adaptive processing for ship detection by high-frequency surface wave radars. IEEE Journal of Oceanic Engineering, 43, 238–257.CrossRefGoogle Scholar
Le Caillec, J.-M., Habonneau, J. and Khenchaf, A. (2019). Ship profile imaging using multipath backscattering. Remote Sensing, 11, 748.CrossRefGoogle Scholar
Lee, J., Tang, H. and Park, J. (2018). Energy efficient Canny edge detector for advanced mobile vision applications. IEEE Transactions on Circuits and Systems for Video Technology, 28, 1037–1046.CrossRefGoogle Scholar
Liu, J. and Gao, Y. (2020). 3D pose estimation for object detection in remote sensing images. Sensors (Basel), 20, 1–16.Google ScholarPubMed
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X. and Pietikäinen, M. (2020a). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128, 261–318.CrossRefGoogle Scholar
Liu, R. W., Nie, J., Garg, S., Xiong, Z., Zhang, Y. and Hossain, M. S. (2020b). Data-driven trajectory quality improvement for promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems. IEEE Internet of Things Journal, 8, 5374–5385.CrossRefGoogle Scholar
Lu, J. W., He, Y. J., Li, H. Y. and Lu, F. L. (2006) Detecting Small Target of Ship at Sea by Infrared Image. Proceedings of the IEEE International Conference on Automation Science & Engineering.CrossRefGoogle Scholar
Min, Z., Jing, M., Liu, D., Xia, Z., Zou, Z. and Shi, Z. (2018). Multi-resolution networks for ship detection in infrared remote sensing images $\star$. Infrared Physics & Technology, 92, 183–189.Google Scholar
Morillas, J. R. A., García, I. C. and Zölzer, U. (2015). Ship detection based on SVM using color and texture features. Proceedings of the 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).CrossRefGoogle Scholar
Mumtaz, A., Jabbar, A., Mahmood, Z., Nawaz, R. and Ahsan, Q. (2016). Saliency based algorithm for ship detection in infrared images. Proceedings of the International Bhurban Conference on Applied Sciences & Technology.CrossRefGoogle Scholar
Nie, S., Jiang, Z., Zhang, H., Cai, B. and Yao, Y. (2018). Inshore Ship Detection Based on Mask R-CNN. Proceedings of the IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.CrossRefGoogle Scholar
Nova, O., Guiffaut, C. and Reineix, A. (2020). Method for the sea clutter characterization in HF surface wave radars from the fields diffracted by the sea surface. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 403–413.CrossRefGoogle Scholar
Pappas, O., Achim, A. and Bull, D. (2018). Superpixel-level CFAR detectors for ship detection in SAR imagery. IEEE Geoscience and Remote Sensing Letters, 15, 1397–1401.CrossRefGoogle Scholar
Ren, Y., Yang, J., Zhang, Q. and Guo, Z. (2020). Ship recognition based on Hu invariant moments and convolutional neural network for video surveillance. Multimedia Tools and Applications, 80, 1343–1373.CrossRefGoogle Scholar
Shafer, S. and Harguess, J. (2015). Sparsity-driven anomaly detection for ship detection and tracking in maritime video. Proceedings of the Spie Defense + Security.Google Scholar
Shao, Z., Wang, L., Wang, Z., Du, W. and Wu, W. (2019). Saliency-aware convolution neural network for ship detection in surveillance video. IEEE Transactions on Circuits and Systems for Video Technology, 30, 781–794.CrossRefGoogle Scholar
Wang, X., Peng, Z., Kong, D., Ping, Z. and He, Y. (2017a). Infrared dim target detection based on total variation regularization and principal component pursuit. Image & Vision Computing, 63, 1–9.CrossRefGoogle Scholar
Wang, C., Bi, F., Zhang, W. and Chen, L. (2017b). An intensity-space domain CFAR method for ship detection in HR SAR images. IEEE Geoscience and Remote Sensing Letters, 14, 529–533.CrossRefGoogle Scholar
Wang, D., Wang, P., Zhang, X., Guo, X., Shu, Y. and Tian, X. (2020). An obstacle avoidance strategy for the wave glider based on the improved artificial potential field and collision prediction model. Ocean Engineering, 206, 107356.CrossRefGoogle Scholar
Wei, C. (2009). Research on Infrared Small Target Detection and Tracking Algorithm. Harbin, PR China: Harbin Institute of Technology.Google Scholar
Xie, B., Hu, L. and Mu, W. (2017). Background Suppression Based on Improved Top-Hat and Saliency Map Filtering for Infrared Ship Detection. Proceedings of the 2017 International Conference on Computing Intelligence and Information System (CIIS).CrossRefGoogle Scholar
Yan, C. Z., Liu, C. and Pang, Y. (2019). Multiscale saliency detection method for ship targets in synthetic aperture radar images. The Journal of Engineering, 2019, 7585–7588.CrossRefGoogle Scholar
Yang, G., Jing, Y., Xiao, C. and Sun, W. (2016). Ship wake detection for SAR images with complex backgrounds based on morphological dictionary learning. Proceedings of the IEEE International Conference on Acoustics.CrossRefGoogle Scholar
Yu, Y., Chen, L., Shu, Y. and Zhu, W. (2021). Evaluation model and management strategy for reducing pollution caused by ship collision in coastal waters. Ocean & Coastal Management, 203, 105446.CrossRefGoogle Scholar
Zhang, Y., Li, Q.-Z. and Zang, F.-N. (2017). Ship detection for visual maritime surveillance from non-stationary platforms. Ocean Engineering, 141, 53–63.CrossRefGoogle Scholar
Zhang, Y., Chen, C., Wu, Q., Lu, Q., Zhang, S., Zhang, G. and Yang, Y. (2018). A kinect-based approach for 3D pavement surface reconstruction and cracking recognition. IEEE Transactions on Intelligent Transportation Systems, 19, 3935–3946.CrossRefGoogle Scholar
Zhang, G., Li, Z., Li, X., Yin, C. and Shi, Z. (2020). A novel salient feature fusion method for ship detection in synthetic aperture radar images. IEEE Access, 8, 215904–215914.CrossRefGoogle Scholar
Zhao, J., Wen, B., Tian, Y., Tian, Z. and Wang, S. (2019). Sea clutter suppression for shipborne HF radar using cross-loop/monopole array. IEEE Geoscience and Remote Sensing Letters, 16, 879–883.CrossRefGoogle Scholar
Zhu, C. and Xue, W. (2015). Detect ships using saliency in infrared images with sea-sky background. Proceedings of the International Conference on Digital Image Processing.Google Scholar
Zimmermann, R. S. and Siems, J. N. (2019). Faster training of mask R-CNN by focusing on instance boundaries. Computer Vision and Image Understanding, 188, 102795.CrossRefGoogle Scholar