A lightweight network for fast ship detection in SAR images

doi:10.7523/j.ucas.2023.017

Abstract

Abstract: In the field of SAR image ship detection based on deep learning, traditional models are usually complex in structure and require a large amount of calculation, which cannot be adapted to low computing power platforms and achieve real-time detection; Convolutional neural networks that rely on preset anchor boxes will lead to a lot of computational redundancy due to the difficulty of setting a reasonable anchor box. To solve these problems, an end-to-end lightweight convolutional neural network based on anchor-free frames is proposed, and a lightweight channel attention module (EESE) is designed and applied to the detection head (ED-Head), which solves the conflict between classification and localization tasks. In addition, an optimized EIOU loss function is proposed, which enables the model to effectively improve the network performance without increasing the inference time. The proposed method is tested on the SSDD dataset, and the experimental results show that compared with YOLOX-nano, AP₅₀ and AP are improved by 2.1% and 7.4%, the CPU latency is only 5.33ms, and the speed is more than twice as fast as YOLOX-nano. The proposed method achieves a balance between accuracy and efficiency.

Key words: (Synthetic Aperture Radar) SAR, ship detection, deep learning, lightweight network, anchor-free detection

CLC Number:

TP39

ZHOU Wenxue, ZHANG Huachun. A lightweight network for fast ship detection in SAR images[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2023.017.

References

[1] Sciotti M, Pastina D, Lombardo P.Exploiting the polarimetric information for the detection of ship targets in non-homogeneous SAR images[C]//IEEE International Geoscience and Remote Sensing Symposium. June 24-28, 2002, Toronto, ON, Canada. IEEE, 2002: 1911-1913. DOI: 10.1109/IGARSS.2002.1026297.
[2] 林旭, 洪峻, 孙显, 等. ScanSAR图像舰船目标快速检测方法[J]. 中国科学院大学学报, 2013, 30(6):793-799. DOI: 10.7523/j.issn.2095-6134.2013.06.012.
[3] Girshick R, Donahue J, Darrell T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. June 23-28, 2014, Columbus, OH, USA. IEEE, 2014: 580-587. DOI: 10.1109/CVPR.2014.81.
[4] He K M, Zhang X Y, Ren S Q, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. DOI: 10.1109/TPAMI.2015.2389824.
[5] Girshick R.Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). December 7-13, 2015, Santiago, Chile. IEEE, 2016: 1440-1448. DOI: 10.1109/ICCV.2015.169.
[6] Ren S Q, He K M, Girshick R, et al.Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
[7] He K M, Gkioxari G, Dollar P, et al.Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386-397. DOI: 10.1109/TPAMI.2018.2844175.
[8] Redmon J, Divvala S, Girshick R, et al.You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[9] Liu W, Anguelov D, Erhan D, et al.SSD: Single Shot MultiBox Detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2.
[10] Redmon J, Farhadi A.YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 6517-6525. DOI: 10.1109/CVPR.2017.690.
[11] Redmon J, Farhadi A. YOLOv3: An incremental improvement[EB/OL].2018: arXiv: 1804.02767.(2018-04-08) [2023-02-20]. https://arxiv.org/abs/1804.02767.
[12] Bochkovskiy A, Wang C-Y, Liao H-Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. arXiv:2004.10934. (2020-04-23) [2023-02-20]. https://arxiv.org/abs/2004.10934.
[13] Iandola F N, Moskewicz M W, Ashraf K, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size[EB/OL]. arXiv:1602.07360. (2016-02-24) [2023-02-20]. https://arxiv.org/abs/1602.07360.
[14] Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. arXiv:1704.04861. (2017-04-17) [2023-02-20]. https://arxiv.org/abs/1704.04861.
[15] Sandler M, Howard A, Zhu M L, et al.MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 4510-4520. DOI: 10.1109/CVPR.2018.00474.
[16] Ma N N, Zhang X Y, Zheng H T, et al.ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design[C]//European Conference on Computer Vision. Cham: Springer, 2018: 122-138. DOI: 10.1007/978-3-030-01264-9_8.
[17] Zhang X Y, Zhou X Y, Lin M X, et al.ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 6848-6856. DOI: 10.1109/CVPR.2018.00716.
[18] Han K, Wang Y H, Tian Q, et al.GhostNet: more features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19, 2020, Seattle, WA, USA. IEEE, 2020: 1577-1586. DOI: 10.1109/CVPR42600.2020.00165.
[19] Ge Z, Liu S, Wang F, et al. YOLOX: Exceeding YOLO series in2021[EB/OL]. arXiv: 2107.08430.(2021-07-18) [2023-02-20]. https://arxiv.org/abs/2107.08430.
[20] Law H, Deng J.CornerNet: Detecting Objects as Paired Keypoints[C]//European Conference on Computer Vision. Cham: Springer, 2018: 765-781. DOI: 10.1007/978-3-030-01264-9_45.
[21] Tian Z, Shen C H, Chen H, et al.FCOS: fully convolutional one-stage object detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27 - November 2, 2019, Seoul, Korea (South). IEEE, 2020: 9626-9635. DOI: 10.1109/ICCV.2019.00972.
[22] 刘方坚, 李媛. 基于视觉显著性的SAR遥感图像NanoDet舰船检测方法[J]. 雷达学报, 2021, 10(6): 885-894. DOI: 10.12000/JR21105.
[23] Liu S, Qi L, Qin H F, et al.Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 8759-8768. DOI: 10.1109/CVPR.2018.00913.
[24] 张晓玲, 张天文, 师君, 等. 基于深度分离卷积神经网络的高速高精度SAR舰船检测[J]. 雷达学报, 2019, 8(6): 841-851. DOI: 10.12000/JR19111.
[25] Song G L, Liu Y, Wang X G.Revisiting the sibling head in object detector[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19, 2020, Seattle, WA, USA. IEEE, 2020: 11560-11569. DOI: 10.1109/CVPR42600.2020.01158.
[26] Hu J, Shen L, Sun G.Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 7132-7141. DOI: 10.1109/CVPR.2018.00745.
[27] Woo S, Park J, Lee J Y, et al.CBAM: Convolutional Block Attention Module[C]//European Conference on Computer Vision. Cham: Springer, 2018: 3-19. DOI: 10.1007/978-3-030-01234-2_1.
[28] 李松, 魏中浩, 张冰尘, 等. 深度卷积神经网络在迁移学习模式下的SAR目标识别[J]. 中国科学院大学学报, 2018, 35(1): 75-83. DOI: 10.7523/j.issn.2095-6134.2018.01.010.
[29] Lee Y, Park J.CenterMask: real-time anchor-free instance segmentation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). June 13-19, 2020, Seattle, WA, USA. IEEE, 2020: 13903-13912. DOI: 10.1109/CVPR42600.2020.01392.
[30] Li D, Hu J, Wang C H, et al.Involution: inverting the inherence of convolution for visual recognition[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 20-25, 2021, Nashville, TN, USA. IEEE, 2021: 12316-12325. DOI: 10.1109/CVPR46437.2021.01214.
[31] Feng C J, Zhong Y J, Gao Y, et al.TOOD: task-aligned one-stage object detection[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). October 10-17, 2021, Montreal, QC, Canada. IEEE, 2022: 3490-3499. DOI: 10.1109/ICCV48922.2021.00349.
[32] Xu S, Wang X, Lv W, et al. PP-YOLOE:An evolved version of YOLO[EB/OL]. arXiv:2203.16250. (2022-03-20) [2023-02-20]. https://arxiv.org/abs/2203.16250.
[33] Rezatofighi H, Tsoi N, Gwak J, et al.Generalized intersection over union: A metric and a loss for bounding box regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA. IEEE, 2020: 658-666. DOI: 10.1109/CVPR.2019.00075.
[34] Zhang Y-F, Ren W, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[EB/OL]. arXiv:2101.08158. (2021-01-20) [2023-02-20]. https://arxiv.org/abs/2101.08158.
[35] 谭显东, 彭辉. 改进YOLOv5的SAR图像舰船目标检测[J]. 计算机工程与应用, 2022, 58(4): 247-254. DOI: 10.3778/j.issn.1002-8331.2108-0308.
[36] Zhang T W, Zhang X L, Li J W, et al.SAR ship detection dataset (SSDD): Official release and comprehensive data analysis[J]. Remote Sensing, 2021, 13(18): 3690. DOI: 10.3390/rs13183690.
[37] Molchanov P, Tyree S, Karras T, et al. Pruning convolutional neural networks for resource efficient transfer learning[EB/OL]. arXiv:1611.06440. (2016-11-19) [2023-02-20]. https://arxiv.org/abs/1611.06440.
[38] Wang Y Y, Wang C, Zhang H, et al.A SAR dataset of ship detection for deep learning under complex backgrounds[J]. Remote Sensing, 2019, 11(7): 765. DOI: 10.3390/rs11070765.