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| Citation: | CHEN Shiqi, WANG Wei, ZHAN Ronghui, et al. A lightweight, arbitrary-oriented SAR ship detector via feature map-based knowledge distillation[J]. Journal of Radars, 2023, 12(1): 140–153. doi: 10.12000/JR21209
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A Lightweight, Arbitrary-oriented SAR Ship Detector via Feature Map-based Knowledge Distillation
DOI: 10.12000/JR21209
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Abstract
In the Synthetic Aperture Radar (SAR) ship target detection task, the targets have a large aspect ratio and dense distribution, and they are arranged in arbitrary directions. The oriented bounding box-based detection methods can output accurate detection results. However, these methods are strongly restricted by high computational complexity, slow inference speed, and large storage consumption, which complicate their deployment on space-borne platforms. To solve the above issues, a lightweight oriented anchor-free-based detection method is proposed by combining feature map and prediction head knowledge distillation. First, we propose an improved Gaussian kernel based on the aspect ratio and angle information so that the generated heatmaps can better describe the shape of the targets. Second, the foreground region enhancement branch is introduced to make the network focus more on foreground features while suppressing the background interference. When training the lightweight student network, the similarity between pixels is treated as transferred knowledge in heatmap distillation. To tackle the imbalance between positive and negative samples in feature distillation, the foreground attention region is applied as a mask to guide the feature distillation process. In addition, a global semantic module is proposed to model the contextual information around pixels, and the background knowledge is combined to further strengthen the feature representation. Experimental results based on HRSID show that our method can achieve 80.71% mAP with only 9.07 M model parameters, and the detection frame rate meets the needs of real-time applications. -
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References
[1] DU Lan, DAI Hui, WANG Yan, et al. Target discrimination based on weakly supervised learning for high-resolution SAR images in complex scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 461–472. doi: 10.1109/TGRS.2019.2937175[2] CHEN Jianlai, ZHANG Junchao, JIN Yanghao, et al. Real-time processing of spaceborne SAR data with nonlinear trajectory based on variable PRF[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5205212. doi: 10.1109/TGRS.2021.3067945[3] GAO Gui, LIU Li, ZHAO Lingjun, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685–1697. doi: 10.1109/TGRS.2008.2006504[4] CRISP D J. The state-of-the-art in ship detection in synthetic aperture radar imagery[R]. DSTO-RR-0272, 2004.[5] 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. doi: 10.3969/j.issn.1001-506X.2018.09.09LI Jianwei, QU Changwen, PENG Shujuan, et al. Ship detection in SAR images based on convolutional neural network[J]. Systems Engineering and Electronics, 2018, 40(9): 1953–1959. doi: 10.3969/j.issn.1001-506X.2018.09.09[6] REN Shaoqing, HE Kaiming, 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] ZHANG Xiaohan, WANG Haipeng, XU Congan, et al. A lightweight feature optimizing network for ship detection in SAR image[J]. IEEE Access, 2019, 7: 141662–141678. doi: 10.1109/ACCESS.2019.2943241[8] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.[9] 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/JR19111ZHANG Xiaoling, ZHANG Tianwen, SHI Jun, et al. High-speed and high-accurate SAR ship detection based on a depthwise separable convolution neural network[J]. Journal of Radars, 2019, 8(6): 841–851. doi: 10.12000/JR19111[10] REDMON J and FARHADI A. YOLOv3: An incremental improvement[J]. arXiv: 1804.02767, 2018.[11] GAO S, LIU Jianming, MIAO Yuhao, et al. A high-effective implementation of ship detector for SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4019005. doi: 10.1109/LGRS.2021.3115121[12] ZHU Mingming, HU Guoping, ZHOU Hao, et al. H2Det: A high-speed and high-accurate ship detector in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 12455–12466. doi: 10.1109/JSTARS.2021.313116[13] FU Jiamei, SUN Xian, WANG Zhirui, et al. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1331–1344. doi: 10.1109/TGRS.2020.3005151[14] TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS: Fully convolutional one-stage object detection[C]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 2020: 9626–9635.[15] 孙忠镇, 戴牧宸, 雷禹, 等. 基于级联网络的复杂大场景SAR图像舰船目标快速检测[J]. 信号处理, 2021, 37(6): 941–951. doi: 10.16798/j.issn.1003-0530.2021.06.005SUN Zhongzhen, DAI Muchen, LEI Yu, et al. Fast detection of ship targets for complex large-scene SAR images based on a cascade network[J]. Journal of Signal Processing, 2021, 37(6): 941–951. doi: 10.16798/j.issn.1003-0530.2021.06.005[16] GUO Haoyuan, YANG Xi, WANG Nannan, et al. A CenterNet++ model for ship detection in SAR images[J]. Pattern Recognition, 2021, 112: 107787. doi: 10.1016/j.patcog.2020.107787[17] ZHOU Xingyi, WANG Dequan, and KRHENBÜHL P. Objects as points[EB/OL]. http://arxiv.org/abs/1904.07850, 2019.[18] AN Quanzhi, PAN Zongxu, LIU Lei, 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. doi: 10.1109/TGRS.2019.2920534[19] YANG Rong, PAN Zhenru, JIA Xiaoxue, et al. A novel CNN-based detector for ship detection based on rotatable bounding box in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 1938–1958. doi: 10.1109/JSTARS.2021.3049851[20] LIN Tsung-Yi, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 2999–3007.[21] 徐英, 谷雨, 彭冬亮, 等. 面向合成孔径雷达图像任意方向舰船检测的改进YOLOv3模型[J]. 兵工学报, 2021, 42(8): 1698–1707. doi: 10.3969/j.issn.1000-1093.2021.08.014XU Ying, GU Yu, PENG Dongliang, et al. An improved YOLOv3 model for arbitrary-oriented ship detection in SAR image[J]. Acta Armamentarii, 2021, 42(8): 1698–1707. doi: 10.3969/j.issn.1000-1093.2021.08.014[22] FU Kun, FU Jiamei, WANG Zhihui, et al. Scattering-keypoint-guided network for oriented ship detection in high-resolution and large-scale SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11162–11178. doi: 10.1109/JSTARS.2021.3109469[23] XU Yongchao, FU Mingtao, WANG Qimeng, et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(4): 1452–1459. doi: 10.1109/TPAMI.2020.2974745[24] WANG Jingdong, SUN Ke, CHENG Tianheng, et al. Deep high-resolution representation learning for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3349–3364. doi: 10.1109/TPAMI.2020.2983686[25] ZHANG Linfeng and MA Kaisheng. Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors[C]. International Conference on Learning Representations, 2020.[26] CAO Yue, XU Jiarui, LIN Stephen, et al. GCNet: Non-local networks meet squeeze-excitation networks and beyond[C]. The IEEE/CVF International Conference on Computer Vision Workshop, Seoul, Korea, 2019: 1971–1980.[27] WEI Shunjun, ZENG Xiangfeng, QU Qizhe, et al. HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 2020, 8: 120234–120254. doi: 10.1109/ACCESS.2020.3005861[28] ZAGORUYKO S and KOMODAKIS N. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer[EB/OL]. http://arxiv.org/abs/1612.03928, 2016.[29] ROMERO A, BALLAS N, KAHOU S E, et al. FitNets: Hints for thin deep nets[EB/OL]. http://arxiv.org/abs/1412.6550, 2014.[30] YI Jingru, WU Pengxiang, LIU Bo, et al. Oriented object detection in aerial images with box boundary-aware vectors[C]. The IEEE Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2021: 2149–2158.[31] MING Qi, ZHOU Zhiqiang, MIAO Lingjuan, et al. Dynamic anchor learning for arbitrary-oriented object detection[EB/OL]. http://arxiv.org/abs/2012.04150, 2020.[32] DING Jian, XUE Nan, LONG Yang, et al. Learning RoI transformer for oriented object detection in aerial images[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 2844–2853.[33] XIE Xingxing, CHENG Gong, WANG Jiabao, et al. Oriented R-CNN for object detection[C]. The IEEE/CVF International Conference on Computer Vision. 2021: 3520–3529.[34] 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. doi: 10.12000/JR19097SUN Xian, WANG Zhirui, SUN Yuanrui, et al. AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852–862. doi: 10.12000/JR19097 -
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- Figure 1. Overall framework of keypoint-based oriented detector
- Figure 2. Structure of HRNet backbone network
- Figure 3. Schematic diagram of distillation structure
- Figure 4. Heatmap visualization
- Figure 5. Precision-Recall curves under different distillation strategies
- Figure 6. Comparison of predicted heatmaps under different distillation strategies
- Figure 7. Changes of detection performance under different IoU thresholds of different distillation strategies
- Figure 8. Precision-Recall curves comparison of different oriented detection methods on HRSID
- Figure 9. Detection results of different oriented detection methods under different scenes
- Figure 10. Migration detection results on large scene images