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A Lightweight, Arbitrary-oriented SAR Ship Detector via Feature Map-based Knowledge Distillation
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摘要: 基于有向边界框的合成孔径雷达(SAR)舰船目标检测器能输出精准的边界框，但仍存在模型计算复杂度高、推理速度慢、存储消耗大等问题，导致其难以在星载平台上部署。基于此该文提出了结合特征图和检测头分支知识蒸馏的无锚框轻量化旋转检测方法。首先，结合目标的长宽比和方向角信息提出改进高斯核，使生成的热度图能更好地刻画目标形状。然后在检测器预测头部引入前景区域增强分支，使网络更关注前景特征且抑制背景杂波的干扰。在训练轻量化网络时，将像素点间的相似度构建为热度图蒸馏知识。为解决特征蒸馏中正负样本不平衡问题，将前景注意力区域作为掩模引导网络蒸馏与目标相关的特征。另外，该文提出全局语义模块对像素进行上下文信息建模，能够结合背景知识加强目标精确表征。基于HRSID数据集的实验结果表明所提方法在模型参数仅有9.07 M的轻量化条件下，mAP能达到80.71%，且检测帧率满足实时应用需求。
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关键词:
- 合成孔径雷达舰船目标检测 /
- 轻量化旋转框检测 /
- 改进高斯核 /
- 前景区域增强 /
- 知识蒸馏
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. -
图 5 不同蒸馏策略下PR曲线比较
Figure 5. Precision-Recall curves under different distillation strategies
图 6 不同蒸馏策略下预测热度图比较
Figure 6. Comparison of predicted heatmaps under different distillation strategies
图 7 不同蒸馏策略下不同IoU阈值下的检测定位精度变化图
Figure 7. Changes of detection performance under different IoU thresholds of different distillation strategies
图 8 HRSID上不同旋转框检测方法的PR曲线比较
Figure 8. Precision-Recall curves comparison of different oriented detection methods on HRSID
图 9 不同旋转框检测方法下不同场景下的检测结果比较
Figure 9. Detection results of different oriented detection methods under different scenes
表 1 HRSID数据集上的消融实验
Table 1. Ablation experiments on HRSID dataset
基线 旋转高斯核 前景注意力引导 准确率 召回率 平均精度 F1分数 √ 0.8540 0.7234 0.7833 0.7759 √ √ 0.8591 0.7579 0.7927 0.8053 √ √ 0.8448 0.7746 0.8033 0.8082 √ √ √ 0.8877 0.7543 0.8120 0.8156 
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表 2 教师和学生检测网络的性能比较
Table 2. Performance comparison of teacher and student detection network
检测网络 骨干网络 参数量(M) 浮点计算量(G) 精度 教师网络 HRNet32 30.53 104.06 0.8120 未蒸馏学生网络 HRNet16 9.07 59.05 0.7402 蒸馏后学生网络 HRNet16 9.07 59.05 0.7596
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表 3 不同蒸馏方法在近岸和远海场景下的检测性能比较
Table 3. Detection performance comparison of different distillation methods under inshore and offsihore scenes
蒸馏方法 近岸 远海 P R F1 AP P R F1 AP Baseline 0.6241 0.5231 0.5691 0.4819 0.9370 0.9282 0.9326 0.9190 Mimic fea 0.6801 0.4924 0.5712 0.4912 0.9382 0.9279 0.9330 0.9194 L2热度图 0.6539 0.5153 0.5764 0.5061 0.9440 0.9321 0.9380 0.9228 改进热度图蒸馏 0.6665 0.5685 0.6136 0.5389 0.9512 0.9321 0.9416 0.9269 AT fea+改进热度图蒸馏 0.7114 0.5491 0.6198 0.5454 0.9533 0.9347 0.9439 0.9272 改进特征+热度图蒸馏 0.7473 0.5664 0.6443 0.5778 0.9571 0.9279 0.9422 0.9255
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表 4 典型旋转检测器上的性能比较
Table 4. Performance comparison on typical oriented detectors
检测器 准确率 召回率 F1值 平均精度 参数量 帧率 RoI Transformer 0.8524 0.7198 0.7805 0.7681 55.26 11.34 YOLOv3-R 0.8225 0.6531 0.7281 0.6907 59.68 9.13 BBAV 0.8462 0.7332 0.7857 0.7720 71.83 19.08 Oriented-RCNN 0.8369 0.7271 0.7781 0.7582 41.82 15.13 DAL 0.8517 0.7603 0.8034 0.7896 36.34 8.06 CenterNet-R 0.8628 0.7381 0.7956 0.7319 34.04 18.30 RetinaNet-R 0.8301 0.6638 0.7377 0.7070 32.33 16.10 本文方法 0.8475 0.7736 0.8089 0.8071 9.07 28.76
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