Abstract
Ship image target detection has important applications for ship management. In recent years, target detection based on deep learning has been widely studied in visual ship target detection. However, due to the difference and overlap of ship targets, the target loss rate is high. In order to solve the problem, this paper proposes a target detection algorithm based on improved RetinaNet for ship image target detection. The cyclical focal loss function and CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively increases ship detection precision by 2.5%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yuan, P.: Study on maritime supervision of illegal maritime transport behavior of “Inland River Vessels” in Sea are. Dalian Marit. Univ. 44(4), 25–28 (2021). (In Chinese)
Huang, J., Jiang, Z.G., Zhang, H.P.: Ship target detection in remote sensing image based on convolutional neural network. J. Beijing Univ. Aeronaut. Astronaut. 09, 132–139 (2017). (In Chinese)
Zwemer, M.H., Wijnhoven, R.G., Peter, H.N. de With.: Ship detection in harbor surveillance based on large-scale data and CNNs. In: Proceedings of 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Funchal, Madeira, pp. 153–160 (2018)
Lian, Y.Y., Wu, X.J.: Research on image super-resolution reconstruction of super deep convolutional neural network. Comput. Eng. 45(10), 217–220 (2019). (In Chinese)
Luo, H., Wu, K., Guo, Z., et al.: Ship detection with wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(7), 1336–1343 (2011). (In Chinese)
Liu, Z., Zhou, F., Bai, X., et al.: Automatic detection of ship target and motion direction in visual images. Int. J. Electron. 100(1), 94–111 (2013). (In Chinese)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Lin, T.Y.: Feature pyramid networks for object detection. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017). (In Chinese)
Girshick, R., Donahue, J., Darrell, T.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, Y., Fan, T. (2023). Ship Target Detection in Remote Sensing Image Based on Improved RetinaNet. In: Patnaik, S., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 349. Springer, Singapore. https://doi.org/10.1007/978-981-99-1230-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-99-1230-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1229-2
Online ISBN: 978-981-99-1230-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)