Jinxiang Guo, Libo Liu, Feng Xu, Bin Zheng. Airport Scene Aircraft Detection Method Based on YOLO v3[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191003
- Laser & Optoelectronics Progress
- Vol. 56, Issue 19, 191003 (2019)
Fig. 1. Detection flow of YOLO v3
Fig. 2. Dilated convolutions. (a) rrate=1; (b) rrate=2; (c) rrate=3
Fig. 3. Backbone network and FPN architecture of the improved YOLO v3
Fig. 4. Structure of dilated convolution residuals. (a) Dilated convolution bottleneck; (b) dilated convolution bottleneck with 1×1 Conv projection
Fig. 5. Two planes of occlusion
Fig. 6. Flow chart of the optimized NMS processing
Fig. 7. Loss curve
Fig. 8. Detecting results of multi-scale small targets by different methods
Fig. 9. Contrast experiments of aircraft detection with different occlusion proportions. (a)(b) Occlusion is close to 20%; (c)(d) occlusion is close to 60%; (e)(f) obvious color characteristics, occlusion is close to 60%; (g)(h) occlusion is close to 80%
Table 1. Airport scene aircraft data sets
|
Table 2. Detection performance comparison of different overlapped proportions
|
Table 3. Performance comparison of various detection methods
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20