Intelligent algorithm of warship’s vital parts detection, trajectory prediction and pose estimation
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摘要:
准确检测与打击舰船要害部位可有效提升反舰导弹毁伤效能。针对舰船要害部位检测精度低、导引误差解算精度不足等问题，提出基于深度学习的舰船要害关键点检测、轨迹预测与导引头位姿估计算法。融合深层语义信息与浅层定位信息，采用SoftPool池化保留细粒度特征，提升多角度多尺度舰船要害部位检测精度；将关键点检测结果与舰船空间结构建立映射，解算导引头三维位姿；引入长短期记忆网络挖掘要害打击点时空特征，实现多尺度舰船要害动态轨迹预测。实验结果表明：所提算法对舰船要害部位检测与轨迹预测精度高，导引头位姿估计结果较准确，满足自主突防视角反舰导弹对复杂海战场的态势感知需求。
Abstract:Accurate detection and attack of warship’s vital parts can effectively improve the damage efficiency of anti-ship missile. Aiming at the problems of low detection accuracy on vital parts and insufficient accuracy of guidance error, this paper proposes an algorithm of warship’s vital parts detection, trajectory prediction and pose estimation based on deep learning. The deep semantic information and shallow positioning information are integrated, and the SoftPool modules are used to preserve fine-grained features. The detection accuracy of multi-angle and multi-scale warship’s vital parts is improved. Combining the detection results with the warship’s space structure can establish the mapping relationship, which is used to calculate the three-dimensional position and posture of the seeker. The long short term memory network is introduced to mine the space-time characteristics of key-points to realize the dynamic trajectory prediction on multi-scale warship. Experimental results show that this algorithm has high accuracy in detection of warship’s vital parts and trajectory prediction. The posture estimation results of the seeker are precise. The situational awareness requirement in complex marine battlefield of autonomous self-piloted anti-ship missiles is satisfied in independent penetration perspective.
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Key words:
- target detection /
- key-points network /
- SoftPool /
- long short term memory /
- pose estimation /
- anti-ship missile
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表 1 实验环境
Table 1. Experimental environment
参数 配置信息 CPU AMD Ryzen 9 3900X CPU显存 32 GB GPU GEFORCE RTX 2080Ti GPU显存 11 GB IDE Pycharm、gedit、vim 系统 Ubuntu 16.04 LTS 语言 Python 加速环境 CUDA10.0，CuDNN7.6 深度学习框架 Pytorch1.0 
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表 3 不同池化方式测试结果
Table 3. Test results of different poolings
池化方式 mAP/% 检测速度/FPS 最大值池化 84.4 29 随机采样池化 85.4 28 空间金字塔池化 85.9 29 SoftPool池化 87.7 27
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表 5 轨迹预测算法对比
Table 5. Comparison of trajectory prediction algorithms
算法 ADE/像素 FDE/像素 关键点检测真值 0.303 2 1.450 6 Kalman Filter 1.139 8 2.671 2 ARIMA 0.963 5 2.045 6 LSTM 0.326 3 1.632 5
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