Classification and recognition of spatio-temporal behavior of ships based on deep learning of trajectory feature images

Table 1. Comparative and analysis of trajectory grid quantization results
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Table 1. Comparative and analysis of trajectory grid quantization results
网格化数量准确率/% 召回率/% F1分数/%
32×32 85.26 75.97 80.35
64×64 89.08 89.87 89.47
128×128 88.45 88.20 88.32
256×256 89.73 90.25 89.99

Table 2. Training results of motion feature combinations

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Table 2. Training results of motion feature combinations

运动特征组合	召回率/%	精确度/%	F1分数/%	准确率/%
组合1	81.93	84.12	83.01	81.78
组合2	82.71	84.32	83.51	82.73
组合3	87.36	87.4	87.37	86.50
组合4	69.23	74.90	71.95	69.10
组合5	84.53	85.11	84.82	84.95
组合6	90.99	91.23	91.11	91.22
组合7	83.13	84.04	83.59	83.81
组合8	68.91	75.50	72.06	67.54
组合9	79.91	80.53	80.22	79.96
组合10	84.06	85.70	84.87	84.89

Table 3. Test set training results

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Table 3. Test set training results

行为类型	总识别数/个	识别正确数/个	召回率/%	精确度/%	F1分数/%
复杂线团	194	193	98.97	99.48	99.22
靠泊	211	160	75.47	84.66	79.80
锚泊	179	147	81.67	72.77	76.96
密集往复	111	105	93.75	98.13	95.89
索套	196	186	94.42	94.42	94.42
无序折线	187	180	95.74	90.91	93.26
转向	165	157	94.58	95.73	95.15
直线	238	223	93.31	93.70	93.50
平均值			90.99	91.23	91.02

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Yu ZHOU, Liang HUANG, Chunhui ZHOU, Yuanqiao WEN, Yamin HUANG, Jiaci WANG. Classification and recognition of spatio-temporal behavior of ships based on deep learning of trajectory feature images[J]. Chinese Journal of Ship Research, 2025, 20(2): 366

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