An Improved YOLOv3 Model for Arbitrary-oriented Ship Detection in SAR Image

doi:10.3969/j.issn.1000-1093.2021.08.014

Abstract

Abstract: An improved YOLOv3 model for arbitrary-oriented ship detection is proposed to realize the simultaneous output of both position and aspect angle estimation information for synthetic aperture radar (SAR) ship detection. The scope of target’s aspect angle which is beneficial for the stability of model parameter regression is defined, and the multi-task loss function is defined based on the predictions of both vertical and rotated bounding boxes. The combinations of prediction results of both vertical and rotated bounding boxes are used to rectify target’s aspect angle estimation for improving the detection performance. The SAR ship detection dataset plus (SSDD+) and high resolution SAR images dataset (HRSID) are used to do performance and transferability tests for the proposed model. The experimental results demonstrate that, for SSDD+dataset, the mean average precision reaches 0.841 when intersection over union equals 0.5 (mAP_0.5); mAP_0.5 can reach 0.530 when HRSID is used to perform transferability tests; the proposed model takes about 25 milliseconds to process one frame when the input resolution of the model is 416×416. High resolution ship collection 2016 (HRSC2016) which is a visual ship recognition dataset is also used to verify the adaptability of the proposed model, and mAP_0.5 reaches 0.888, which is superior to some known models. The proposed model can be applied to detect SAR ships in pure sea background, and can meet real-time requirement of ship detection.

Key words: syntheticapertureradarimage, shipdetection, YOLOv3model, aspectangleestimation, multi-taskloss

CLC Number:

TP753

XU Ying， GU Yu， PENG Dongliang， LIU Jun， CHEN Huajie. An Improved YOLOv3 Model for Arbitrary-oriented Ship Detection in SAR Image[J]. Acta Armamentarii, 2021, 42(8): 1698-1707.

References

［1］马啸, 邵利民, 金鑫, 等. 舰船目标识别技术研究进展[J]. 科技导报, 2019, 37(24): 65-78.
MA X, SHAO L M, JIN X, et al. Advances in ship target recognition technology[J]. Science & Technology Review, 2019, 37(24): 65-78. (in Chinese)
[2］吴良斌. SAR图像处理与目标识别[M]. 北京: 航空工业出版社, 2013.
WU L B. SAR image processing and target recognition[M]. Beijing: Aviation Industry Press, 2013. (in Chinese)
[3］王超, 张红, 吴樊, 等. 高分辨率SAR图像船舶目标检测与分类[M]. 北京: 科学出版社, 2013.
WANG C, ZHANG H, WU F, et al. High-resolution SAR image ship detection and classification[M]. Beijing: Science Press, 2013. (in Chinese)
[4］高贵, 周蝶飞, 蒋咏梅, 等. SAR图像目标检测研究综述[J]. 信号处理, 2008, 24(6): 971-981.
GAO G, ZHOU D F, JIANG Y M, et al. Study on target detection in SAR image: a survey[J]. Signal Processing, 2008, 24(6): 971-981. (in Chinese)
[5］ El-DARYMLI K, MCGUIRE P, POWER D, et al. Target detection in synthetic aperture radar imagery: a state-of-the-art survey[J]. Journal of Applied Remote Sensing, 2013, 7(1): 071598.
[6］杜兰, 王兆成, 王燕, 等. 复杂场景下单通道SAR目标检测及鉴别研究进展综述[J]. 雷达学报, 2020, 9(1): 34-54.
DU L, WANG Z C, WANG Y, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes[J]. Journal of Radars, 2020, 9(1): 34-54. (in Chinese)
[7］ LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521: 436-444.
[8］ LIU L, OUYANG W L, WANG X G, et al. Deep learning for generic object detection: a survey[J]. International Journal of Computer Vision, 2020, 128: 261-318.
[9］ LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]∥Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands:Springer, 2016: 21-37.
[10］ REDMON J, FARHADI A. YOLOv3: An incremental improvement: arXiv: 1804. 02767v1[R/OL]. Ithaca, NY, US: Cornell University, (2018-04-08) [2020-08-06]. https:∥arxiv.org/abs/1804.02767.
[11］ 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.
[12］李健伟, 曲长文, 彭书娟, 等. 基于卷积神经网络的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2018, 40(9): 1953-1959.
LI J W, QU C W, PENG S J, et al. Ship detection in SAR images based on convolutional neural network[J]. Systems Engineering and Electronics, 2018, 40(9): 1953-1959. (in Chinese)
[13］ HUANG L Q, LIU B, LI B Y, et al. OpenSARShip: a dataset dedicated to Sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195-208.
[14］ 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.
[15］孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0：高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852-862.
SUN X, WANG Z R, SUN Y R, et al. AIR-SARShip-1.0: high-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852-862. (in Chinese)
[16］ WEI S J, ZENG X F, QU Q Z, et al. HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 2020, 8: 120234-120254.
[17］赵保军, 李珍珍, 赵博雅, 等. 基于低复杂度卷积神经网络的星载SAR舰船检测[J]. 北京交通大学学报, 2017, 41(6): 1-7.
ZHAO B J, LI Z Z, ZHAO B Y, et al. Spaceborne SAR ship detection based on low complexity convolution neural network［J］. Journal of Beijing Jiaotong University, 2017, 41(6):1-7. (in Chinese)
[18］李健伟, 曲长文, 彭书娟, 等. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143-149.
LI J W, QU C W, PENG S J, et al. Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics and Information Technology, 2019, 41(1): 143-149. (in Chinese)
[19］ JIAO J, ZHANG Y, SUN H, et al. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection[J]. IEEE Access, 2018, 6:20881-20892.
[20］ ZHAO J P, ZHANG Z H, YU W X, et al. A cascade coupled convolutional neural network guided visual attention method for ship detection from SAR images[J]. IEEE Access, 2018, 6: 50693-50708.
[21］ CHEN C, HE C, HU C H, et al. A deep neural network based on an attention mechanism for SAR ship detection in multiscale and complex scenarios[J]. IEEE Access, 2019, 7: 104848-104863.
[22］ ZHANG X H, WANG H P, XU C A, et al. A lightweight feature optimizing network for ship detection in SAR image[J]. IEEE Access, 2019, 7: 141662-141678.
[23］杨龙, 苏娟, 李响. 基于深度卷积神经网络的SAR舰船目标检测[J]. 系统工程与电子技术, 2019, 41(9): 1990-1997.
YANG L, SU J, LI X. Ship detection in SAR images based on deep convolutional neural network[J]. Systems Engineering and Electronics, 2019, 41(9): 1990-1997. (in Chinese)
[24］ CUI Z Y, LI Q, CAO Z J, et al. Dense attention pyramid networks for multi-scale ship detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8983-8997.
[25］ CHEN C, HE C, HU C H, et al. MSARN: a deep neural network based on an adaptive recalibration mechanism for multiscale and arbitrary-oriented SAR ship detection[J]. IEEE Access, 2019, 7: 159262-159283.
[26］ AN Q Z, PAN Z X, LIU L, et al. DRBox-v2: an improved detector with rotatable boxes for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8333-8349.
[27］ LIU Z K, YUAN L, WENG L B, et al. A high resolution optical satellite image dataset for ship recognition and some new baselines[C]∥Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. Porto, Portugal: SciTePress, 2017: 324-331.
[28］ LIN T Y, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: IEEE, 2017: 936-944.
[29］ ZHANG H Y, CISSE M, DAUPHIN Y N, et al. mixup: beyond empirical risk minimization[C]∥Proceedings of International Conference on Learning Representaitons. Vancouver, BC, Canada: the Association for the Advancement of Artificial Intelligence, 2018.
[30］仲伟峰, 郭峰, 向世明, 等. 旋转矩形区域的遥感图像舰船目标检测模型[J]. 计算机辅助设计与图形学学报, 2019, 31(11): 1935-1945.
ZHONG W F, GUO F, XIANG S M, et al. Ship detection in remote sensing based with rotated rectangular region[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(11):1935-1945. (in Chinese)

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