High Power Laser and Particle Beams, Volume. 36, Issue 6, 069001(2024)
Research of aircraft pose estimation based on neural network feature line extraction
Figures & Tables(12)
Fig. 8. Relation between distance and prediction error in different situations
Table 1. Results of fully connected head
View tableView in ArticleTable 1. Results of fully connected head
No. output feature precision/% 1 $ k,b $ 36.39 2 $ k,d $ 71.35 3 $ \theta ,b $ 18.57 4 $ \theta ,d $ 73.15 5 $ \theta ,x,y $ 76.56
Table 2. Precision of different backbones
View tableView in ArticleTable 2. Precision of different backbones
backbone precision/% type 1 type 2 type 3 type 4 Resnet152[ 16 ]85.37 83.25 75.54 74.33 Resnext152[ 17 ]86.75 88.29 79.21 78.13 Hrnet48[ 18 ]88.59 87.30 84.26 83.30 Densenet201[ 19 ]91.13 90.81 84.26 84.03 Mobilenetv2[ 20 ]84.02 82.26 74.93 72.37
Table 3. Average error of pose estimation in different situations
View tableView in ArticleTable 3. Average error of pose estimation in different situations
situation 7 lines model error/% 17 lines model error/% pure background 1.04 (1.24°) 0.52 (1.53°) complex background 1.05 (1.21°) 0.47 (0.57°) complex background and noises 3.49 (4.61°) 0.93 (2.11°)
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Changjun Chen, Dan Tang, Hao Yang, Anqing You, Xudong Pan. Research of aircraft pose estimation based on neural network feature line extraction[J]. High Power Laser and Particle Beams, 2024, 36(6): 069001
Paper Information
Category: Advanced Interdisciplinary Science
Received: Jan. 23, 2024
Accepted: Mar. 6, 2024
Published Online: Jun. 3, 2024
The Author Email: Tang Dan (278803565@qq.com)
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