IR-Net: An improved RetinaNet-based ship detection detector

Chen Feng

doi:10.54097/hset.v7i.1087

Authors

Chen Feng

DOI:

https://doi.org/10.54097/hset.v7i.1087

Keywords:

IR-Net, RetinaNet, ship detection detector

Abstract

The task of ship object detection is of great importance to research. Ship object at sea is key to marine monitoring, real-time rescue, and wartime attack. Whether the ship object can be identified quickly and accurately to support the commander's decision-making is largely related to the success of the maritime mission. However, the dense arrangement and arbitrary direction of ships make it more difficult than the general object detection. Therefore, this paper proposes a rotation detector to deal with these problems. Furthermore, ship detection faces some chanllenges such as the complex background of offshore port images, the large ratio of ship length to width, and the potential of being blocked by clouds. Therefore, this paper proposes a rotatable bounding box based on RetinaNet, named Improved RetinaNet (IR-Net). With the deeper ResNet as its backbone network, this network extracts deeper object features. Furthermore, DCM (deformable convolutional module) is added to the network, so that the sampling points of the network can change with the shape of the object ship, thus extracting the object features more effectively. Finally, in this paper, the Cutout data enhancement strategy in the network is added, which effectively improves its detection accuracy in the case of object occlusion. Ablation experiments show that the IR-Net proposed in this paper has a more accurate detection performance on the HRSC2016 ship dataset.

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References

An, Y. (2014). Research on ship object detection and recognition sea battlefield. (Doctoral Dissertation). Harbin Engineering University, Harbin, Heilongjiang. DOI:10.7666/d.D749653.

Liu, G., Zhang, X. & Meng, J. (2019). A small ship object detection method based on polarimetric SAR. Remote Sensing, 11(24), 2938.

Zhao, Z. Q., Zheng, P., Xu, S. et al. (2019). Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems, 30(11): 3212-3232.

Girshick, R., Donahue, J., Darrell, T. et al. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 580-587.

Redmon, J., Divvala, S., Girshick, R. et al. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.

Liu, W., Anguelov, D., Erhan, D. et al. (2016). Ssd: Single shot multibox detector. European conference on computer vision. Springer, Cham, 21-37.

Liu, L., Pan, Z., Lei, B. (2017). Learning a rotation invariant detector with rotatable bounding box. arXiv preprint arXiv:1711.09405.

Lei, S., Qiu, X., Ding, C. et al. (2021). A feature enhancement method based on the sub-aperture decomposition for rotating frame ship detection in SAR images. IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 3573-3576.

Liu, W., Ma, L. & Chen, H. (2018). Arbitrary-oriented ship detection framework in optical remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 15(6), 937-941.

Wang, T. & Li, Y. (2021). Rotation-Invariant Task-Aware Spatial Disentanglement in Rotated Ship Detection Based on the Three-Stage Method. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12.

Li, L., Zhou, Z., Wang, B. et al. (2020). A novel CNN-based method for accurate ship detection in HR optical remote sensing images via rotated bounding box. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 686-699.

Liu, Z., Hu, J., Weng, L. et al. (2017). Rotated region based CNN for ship detection. 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 900-904.

He, K., Zhang, X., Ren, S. et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.

Dai, J., Qi, H., Xiong, Y., et al. (2017). Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision, 764-773.

DeVries, T. & Taylor, G. W. Improved regularization of convolutional neural networks with Cuout. arXiv preprint arXiv, 1708.04552, 2017.