Advanced search
Publication Cover
Connection Science Volume 35, 2023 - Issue 1
Submit an article Journal homepage
1,097
Views
0
CrossRef citations to date
0
Altmetric
Research Article

NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention

Hao WangCollege of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of ChinaView further author information
,
Dezhi HanCollege of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of ChinaCorrespondence[email protected]
View further author information
,
Mingming CuiCollege of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of ChinaView further author information
&
Chongqing ChenCollege of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of ChinaView further author information
Pages 1-32 | Received 30 Apr 2023, Accepted 06 Sep 2023, Published online: 04 Oct 2023

References

  • Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Chen, C., Han, D., & Chang, C.-C. (2022). CAAN: Context-aware attention network for visual question answering. Pattern Recognition, 132, 108980. https://doi.org/10.1016/j.patcog.2022.108980
  • Chen, C., Han, D., & Shen, X. (2023). CLVIN: Complete language-vision interaction network for visual question answering. Knowledge-Based Systems, 275, 110706. https://doi.org/10.1016/j.knosys.2023.110706
  • Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2019). Rethinking atrous convolution for semantic image segmentation. arXiv 2017. arXiv preprint arXiv:1706.05587, 2.
  • Chen, P., Zhou, H., Li, Y., Liu, P., & Liu, B. (2023). A novel deep learning network with deformable convolution and attention mechanisms for complex scenes ship detection in SAR images. Remote Sensing, 15(10), 2589. https://doi.org/10.3390/rs15102589
  • Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., & Sun, J. (2021). You only look one-level feature. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Chen, S., Zhan, R., Wang, W., & Zhang, J. (2021). Learning slimming SAR ship object detector through network pruning and knowledge distillation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 1267–1282. https://doi.org/10.1109/JSTARS.2020.3041783
  • Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K. (2021). Attentional feature fusion. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.
  • Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019). Centernet: Keypoint triplets for object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • Farah, F., Laroussi, T., & Madjidi, H. (2022). A fast ship detection algorithm based on automatic censoring for multiple target situations in SAR images. 2022 7th International Conference on Image and Signal Processing and their Applications (ISPA).
  • Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021). Tood: Task-aligned one-stage object detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.
  • Ghiasi, G., Lin, T.-Y., & Le, Q. V. (2019). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision.
  • Guo, H., Yang, X., Wang, N., Song, B., & Gao, X. (2020). A rotational libra R-CNN method for ship detection. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5772–5781. https://doi.org/10.1109/TGRS.2020.2969979
  • Han, D., Pan, N., & Li, K.-C. (2022). A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection. IEEE Transactions on Dependable and Secure Computing, 19(1), 316–327. https://doi.org/10.1109/TDSC.2020.2977646
  • Han, L., Liu, D., & Guan, D. (2022). Ship detection in SAR images by saliency analysis of multiscale superpixels. Remote Sensing Letters, 13(7), 708–715. https://doi.org/10.1080/2150704X.2022.2068988
  • Hou, Q., Zhou, D., & Feng, J. (2021). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Hu, Q., Hu, S., Liu, S., Xu, S., & Zhang, Y.-D. (2022). Finet: A feature interaction network for SAR ship object-level and pixel-level detection. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15.
  • Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning.
  • Jiang, M., Gu, L., Li, X., Gao, F., & Jiang, T. (2023). Ship contour extraction from SAR images based on faster R-CNN and chan–vese model. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14.
  • Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A review of yolo algorithm developments. Procedia Computer Science, 199, 1066–1073. https://doi.org/10.1016/j.procs.2022.01.135
  • Karachristos, K., & Anastassopoulos, V. (2023). Automatic ship detection using PolSAR imagery and the double scatterer model. Geomatics, 3(1), 174–187. https://doi.org/10.3390/geomatics3010009
  • Ke, X., Zhang, X., Zhang, T., Shi, J., & Wei, S. (2021). SAR ship detection based on an improved faster R-CNN using deformable convolution. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.
  • Leng, X., Ji, K., Yang, K., & Zou, H. (2015). A bilateral CFAR algorithm for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 12(7), 1536–1540. https://doi.org/10.1109/LGRS.2015.2412174
  • Li, D., Han, D., Weng, T.-H., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K.-C. (2022). Blockchain for federated learning toward secure distributed machine learning systems: A systemic survey. Soft Computing, 26(9), 4423–4440. https://doi.org/10.1007/s00500-021-06496-5
  • Li, D., Han, D., Zheng, Z., Weng, T.-H., Li, H., Liu, H., Castiglione, A., & Li, K.-C. (2022). MOOCschain: A blockchain-based secure storage and sharing scheme for MOOCs learning. Computer Standards & Interfaces, 81, 103597. https://doi.org/10.1016/j.csi.2021.103597
  • Li, H., Han, D., & Tang, M. (2022). A privacy-preserving storage scheme for logistics data with assistance of blockchain. IEEE Internet of Things Journal, 9(6), 4704–4720. https://doi.org/10.1109/JIOT.2021.3107846
  • Li, J., Han, D., Wu, Z., Wang, J., Li, K.-C., & Castiglione, A. (2023). A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control. Future Generation Computer Systems, 142, 195–211. https://doi.org/10.1016/j.future.2022.12.037
  • Li, S., Fu, X., & Dong, J. (2022). Improved ship detection algorithm based on YOLOX for SAR outline enhancement image. Remote Sensing, 14(16), 4070. https://doi.org/10.3390/rs14164070
  • Li, T., Liu, Z., Xie, R., & Ran, L. (2018). An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1), 184–194. https://doi.org/10.1109/JSTARS.2017.2764506
  • Li, Y., Zhang, S., & Wang, W.-Q. (2020). A lightweight faster R-CNN for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
  • Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision.
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14.
  • Ma, F., Sun, X., Zhang, F., Zhou, Y., & Li, H.-C. (2022). What catch your attention in SAR images: Saliency detection based on soft-superpixel lacunarity cue. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–17.
  • Miao, T., Zeng, H., Yang, W., Chu, B., Zou, F., Ren, W., & Chen, J. (2022). An improved lightweight RetinaNet for ship detection in SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4667–4679. https://doi.org/10.1109/JSTARS.2022.3180159
  • Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
  • Sun, Z., Dai, M., Leng, X., Lei, Y., Xiong, B., Ji, K., & Kuang, G. (2021). An anchor-free detection method for ship targets in high-resolution SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7799–7816. https://doi.org/10.1109/JSTARS.2021.3099483
  • Tan, M., Pang, R., & Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Tian, Q., Han, D., Li, K.-C., Liu, X., Duan, L., & Castiglione, A. (2020). An intrusion detection approach based on improved deep belief network. Applied Intelligence, 50(10), 3162–3178. https://doi.org/10.1007/s10489-020-01694-4
  • Tian, Z., Shen, C., Chen, H., & He, T. (2019). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • Wang, R., Xu, F., Pei, J., Wang, C., Huang, Y., Yang, J., & Wu, J. (2019). An improved faster R-CNN based on MSER decision criterion for SAR image ship detection in harbor. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium.
  • Wei, S., Zeng, X., Qu, Q., Wang, M., Su, H., & Shi, J. (2020). HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation. Ieee Access, 8, 120234–120254. https://doi.org/10.1109/ACCESS.2020.3005861
  • Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV).
  • Xia, R., Chen, J., Huang, Z., Wan, H., Wu, B., Sun, L., Yao, B., Xiang, H., & Xing, M. (2022). CRTranssar: A visual transformer based on contextual joint representation learning for SAR ship detection. Remote Sensing, 14(6), 1488. https://doi.org/10.3390/rs14061488
  • Xu, Z., Gao, R., Huang, K., & Xu, Q. (2022). Triangle distance IoU loss, attention-weighted feature pyramid network, and rotated-SARShip dataset for arbitrary-oriented SAR ship detection. Remote Sensing, 14(18), 4676. https://doi.org/10.3390/rs14184676
  • Yang, S., An, W., Li, S., Wei, G., & Zou, B. (2022). An improved FCOS method for ship detection in SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 8910–8927. https://doi.org/10.1109/JSTARS.2022.3213583
  • Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.
  • Zhai, L., Li, Y., & Su, Y. (2016). Inshore ship detection via saliency and context information in high-resolution SAR images. IEEE Geoscience and Remote Sensing Letters, 13(12), 1870–1874. https://doi.org/10.1109/LGRS.2016.2616187
  • Zhang, L., Zhang, Z., Lu, S., Xiang, D., & Su, Y. (2022). Fast superpixel-based non-window CFAR ship detector for SAR imagery. Remote Sensing, 14(9), 2092. https://doi.org/10.3390/rs14092092
  • Zhang, S., Chi, C., Yao, Y., Lei, Z., & Li, S. Z. (2020). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Zhang, T., & Zhang, X. (2019). High-speed ship detection in SAR images based on a grid convolutional neural network. Remote Sensing, 11(10), 1206. https://doi.org/10.3390/rs11101206
  • Zhang, T., Zhang, X., & Ke, X. (2021). Quad-FPN: A novel quad feature pyramid network for SAR ship detection. Remote Sensing, 13(14), 2771. https://doi.org/10.3390/rs13142771
  • Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., & Su, H. (2021). SAR ship detection dataset (SSDD): Official release and comprehensive data analysis. Remote Sensing, 13(18), 3690. https://doi.org/10.3390/rs13183690
  • Zhang, T., Zhang, X., Shi, J., Wei, S., Wang, J., & Li, J. (2020). Balanced feature pyramid network for ship detection in synthetic aperture radar images. 2020 IEEE Radar Conference (RadarConf20).
  • Zhang, X., Wan, F., Liu, C., Ji, X., & Ye, Q. (2022). Learning to match anchors for visual object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 3096–3109. https://doi.org/10.1109/TPAMI.2021.3050494
  • Zhao, Y., Zhao, L., Xiong, B., & Kuang, G. (2020). Attention receptive pyramid network for ship detection in SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2738–2756. https://doi.org/10.1109/JSTARS.2020.2997081
  • Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence.
  • Zhou, K., Zhang, M., Wang, H., & Tan, J. (2022). Ship detection in SAR images based on multi-scale feature extraction and adaptive feature fusion. Remote Sensing, 14(3), 755. https://doi.org/10.3390/rs14030755
  • Zhou, X., & Li, T. (2023). Ship detection in PolSAR images based on a modified polarimetric notch filter. Electronics, 12(12), 2683. https://doi.org/10.3390/electronics12122683
  • Zhu, M., Hu, G., Zhou, H., Wang, S., Feng, Z., & Yue, S. (2022). A ship detection method via redesigned FCOS in large-scale SAR images. Remote Sensing, 14(5), 1153. https://doi.org/10.3390/rs14051153
  • Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.