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Research Article

FastPFM: a multi-scale ship detection algorithm for complex scenes based on SAR images

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Article: 2313854 | Received 27 Jul 2023, Accepted 30 Jan 2024, Published online: 19 Feb 2024

References

  • Cai, Z., & Vasconcelos, N. (2019). Cascade R-CNN: High quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1483–1498. https://doi.org/10.1109/TPAMI.2019.2956516
  • Chen, C., Han, D., & Chang, C.-C. (2024). MPCCT: Multimodal vision-language learning paradigm with context-based compact transformer. Pattern Recognition, 147, 110084. https://doi.org/10.1016/j.patcog.2023.110084
  • Chen, C., Han, D., & Chang, C.-C. J. P. R. (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. (2023a). 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, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., & Chan, S.-H. G. (2023). Run, don't walk: Chasing higher FLOPS for faster neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Chen, J., Wang, Q., Peng, W., Xu, H., Li, X., & Xu, W. (2022). Disparity-based multiscale fusion network for transportation detection. IEEE Transactions on Intelligent Transportation Systems, 23(10), 18855–18863. https://doi.org/10.1109/tits.2022.3161977
  • 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.
  • Du, Z., Liu, R., Liu, N., & Chen, P. (2008). A New Method for Ship Detection in SAR Imagery Based on Combinatorial PNN Model. 2008 First International Conference on Intelligent Networks and Intelligent Systems.
  • 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).
  • Fu, J., Sun, X., Wang, Z., & Fu, K. (2020). An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1331–1344. https://doi.org/10.1109/TGRS.2020.3005151
  • Gao, F., He, Y., Wang, J., Hussain, A., & Zhou, H. J. R. S. (2020). Anchor-free convolutional network with dense attention feature aggregation for ship detection in SAR images. Remote Sensing, 12(16), 2619. https://doi.org/10.3390/rs12162619
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). YOLOX: Exceeding YOLO series in 2021. arXiv preprint arXiv:2107.08430. https://doi.org/10.48550/arXiv.2107.08430
  • Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision.
  • Han, D., Pan, N., & Li, K.-C. (2020). 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, D., Zhu, Y., Li, D., Liang, W., Souri, A., & Li, K.-C. (2021). A blockchain-based auditable access control system for private data in service-centric IoT environments. IEEE Transactions on Industrial Informatics, 18(5), 3530–3540. https://doi.org/10.1109/TII.2021.3114621
  • 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. https://doi.org/10.1109/TGRS.2022.3222636
  • Jiang, J., Fu, X., Qin, R., Wang, X., & Ma, Z. (2021). High-speed lightweight ship detection algorithm based on YOLO-v4 for three-channels RGB SAR image. Remote Sensing, 13(10), 1909. https://doi.org/10.3390/rs13101909
  • 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. (2021). 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
  • 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.
  • Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13.
  • Lin, Z., Ji, K., Leng, X., & Kuang, G. (2019). Squeeze and excitation rank faster R-CNN for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 16(5), 751–755. https://doi.org/10.1109/LGRS.2018.2882551
  • Liu, T., Yang, Z., Yang, J., & Gao, G. (2019). CFAR ship detection methods using compact polarimetric SAR in a K-Wishart distribution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3737–3745. https://doi.org/10.1109/JSTARS.2019.2923009
  • 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.
  • Migliaccio, M., Gambardella, A., & Nunziata, F. (2008, 27–29 May 2008). Ship detection over single-look complex SAR images. 2008 IEEE/OES US/EU-Baltic International Symposium.
  • Peng, H., & Tan, X. (2022). Improved YOLOX’s anchor-free SAR image ship target detection. IEEE Access, 10, 70001–70015. https://doi.org/10.1109/ACCESS.2022.3188387
  • Qian, L., Zheng, Y., Li, L., Ma, Y., Zhou, C., & Zhang, D. (2022). A new method of inland water ship trajectory prediction based on long short-term memory network optimized by genetic algorithm. Applied Sciences, 12(8), 4073.
  • Rai, M. C. E., Giraldo, J. H., Al-Saad, M., Darweech, M., & Bouwmans, T. (2022). SemiSegSAR: A semi-supervised segmentation algorithm for ship SAR images. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2022.3185306
  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28https://proceedings.neurips.cc/paper_files/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html
  • Steenson, B. O. (1968). Detection performance of a mean-level threshold. IEEE Transactions on Aerospace and Electronic Systems, AES-4, (4), 529–534. https://doi.org/10.1109/TAES.1968.5409020
  • 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
  • Sun, Z., Leng, X., Lei, Y., Xiong, B., Ji, K., & Kuang, G. J. R. S. (2021). BiFA-YOLO: A novel YOLO-based method for arbitrary-oriented ship detection in high-resolution SAR images. Remote Sensing, 13(21), 4209. https://doi.org/10.3390/rs13214209
  • Tang, G., Zhuge, Y., Claramunt, C., & Men, S. (2021). N-YOLO: A SAR ship detection using noise-classifying and complete-target extraction. Remote Sensing, 13(5), 871. https://doi.org/10.3390/rs13050871
  • 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, C., Shi, J., Zou, Z., Wang, W., & Zhou, Y. (2021). A semi-supervised SAR ship detection framework via label propagation and consistent augmentation. IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4884–4887.
  • Wang, C.-Y., Liao, H.-Y. M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., & Yeh, I.-H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.
  • Wang, H., Han, D., Cui, M., & Chen, C. (2023). NAS-YOLOX: A SAR ship detection using neural architecture search and multi-scale attention. Connection Science, 35(1), 1–32. https://doi.org/10.1080/09540091.2023.2257399
  • Wang, J., Leng, X., Sun, Z., Zhang, X., & Ji, K. (2023a). Fast and accurate refocusing for moving ships in SAR imagery based on FrFT. Remote Sensing, 15(14), 3656. https://www.mdpi.com/2072-4292/15/14/3656.
  • Wang, J., Leng, X., Sun, Z., Zhang, X., & Ji, K. (2023b). Refocusing swing ships in SAR imagery based on spatial-variant defocusing property. Remote Sensing, 15(12), 3159.
  • Wang, S., Gao, S., Zhou, L., Liu, R., Zhang, H., Liu, J., Jia, Y., & Qian, J. (2022). YOLO-SD: Small ship detection in SAR images by multi-scale convolution and feature transformer module. Remote Sensing, 14(20), 5268. https://doi.org/10.3390/rs14205268
  • Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • 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
  • Yasir, M., Shanwei, L., Mingming, X., Hui, S., Hossain, M. S., Colak, A. T. I., Wang, D., Jianhua, W., & Dang, K. B. (2023). Multi-scale ship target detection using SAR images based on improved YOLOv5. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.1086140
  • 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, J., Sheng, W., Zhu, H., Guo, S., & Han, Y. (2023a). MLBR-YOLOX: An efficient SAR ship detection network with multilevel background removing modules. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5331–5343. https://doi.org/10.1109/JSTARS.2023.3280741
  • Zhang, T., Zhang, X., & Ke, X. (2021aa). 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., Ahmad, I., Pan, D., Liu, C., Zhou, Y., Shi, J., & Wei, S. (2021). SAR ship Detection Dataset (SSDD): Official release and comprehensive data analysis. Remote Sensing, 13(18), 3690. https://doi.org/10.3390/rs13183690
  • Zhang, X., Huo, C., Xu, N., Jiang, H., Cao, Y., Ni, L., & Pan, C. (2021). Multitask learning for ship detection from synthetic aperture radar images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 8048–8062. https://doi.org/10.1109/JSTARS.2021.3102989
  • Zhang, Y., Han, D., & Chen, P. (2023b). Swin-PAFF: A SAR ship detection network with contextual cross-information fusion. Computers, Materials & Continua, 77(2), 2657–2675. https://doi.org/10.32604/cmc.2023.042311
  • Zhang, Y., & Hao, Y. (2022). A survey of SAR image target detection based on convolutional neural networks. Remote Sensing, 14(24), 6240. https://doi.org/10.3390/rs14246240
  • Zhao, W., Syafrudin, M., & Fitriyani, N. L. (2023). CRAS-YOLO: A novel multi-category vessel detection and classification model based on YOLOv5s algorithm. IEEE Access, 11, 11463–11478. https://doi.org/10.1109/ACCESS.2023.3241630
  • 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, Y., Li, L., Qian, L., Cheng, B., Hou, W., & Zhuang, Y. (2023). Sine-SSA-BP ship trajectory prediction based on chaotic mapping improved sparrow search algorithm. Sensors, 23(2), 704. http://doi.org/10.3390/s23020704
  • Zheng, Y., Liu, P., Qian, L., Qin, S., Liu, X., Ma, Y., & Cheng, G. (2022). Recognition and depth estimation of ships based on binocular stereo vision. Journal of Marine Science and Engineering, 10(8), 1153. https://doi.org/10.3390/jmse10081153
  • Zheng, Y., Zhang, Y., Qian, L., Zhang, X., Diao, S., Liu, X., Cao, J., & Huang, H. (2023). A lightweight ship target detection model based on improved YOLOv5s algorithm. PLoS One, 18(4), e0283932. https://doi.org/10.1371/journal.pone.0283932
  • Zhou, Y., Fu, K., Han, B., Yang, J., Pan, Z., Hu, Y., & Yin, D. (2023). D-MFPN: A doppler feature matrix fused with a multilayer feature pyramid network for SAR ship detection. Remote Sensing, 15(3), 626. http://doi.org/10.3390/rs15030626
  • Zhou, Y., Jiang, X., Chen, Z., Chen, L., & Liu, X. (2023). A semisupervised arbitrary-oriented SAR ship detection network based on interference consistency learning and pseudolabel calibration. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5893–5904. http://doi.org/10.1109/JSTARS.2023.3284667
  • Zhu, H., Xie, Y., Huang, H., Jing, C., Rong, Y., & Wang, C. J. S. (2021). DB-YOLO: A duplicate bilateral YOLO network for multi-scale ship detection in SAR images. Sensors, 21(23), 8146. https://doi.org/10.3390/s21238146
  • Zhu, L., Wang, X., Ke, Z., Zhang, W., & Lau, R. W. (2023). BiFormer: Vision Transformer with Bi-Level Routing Attention. (Ed.), (Eds.). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Zhu, M., Hu, G., Zhou, H., & Wang, S. (2022). Multiscale ship detection method in SAR images based on information compensation and feature enhancement. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13. https://doi.org/10.1109/tgrs.2022.3202495
  • 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. https://doi.org/10.48550/arXiv.2010.04159
  • Zong, C., & Wan, Z. (2022). Container ship cell guide accuracy check technology based on improved 3D point cloud instance segmentation. Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, 73(1), 23–35.