Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement
Abstract
:1. Introductory
- (1)
- An efficient bidirectional-path multiscale fusion and attention network (EBMA-Net) is proposed. It realizes multiple cross-level feature-fusion operations, captures aircraft features more richly and comprehensively, and can effectively solve the problems of discrete aircraft, scale inhomogeneity and complex background interference.
- (2)
- The construction of an efficient multiscale channel attention fusion module (EMCA), which effectively learns multiscale spatial information and accurately captures small aircraft target features.
- (3)
- An efficient dual-channel attention module is proposed, namely, squeeze efficient channel attention (SECA), to reduce the interference of complex background and scattering noise, enhance the robustness of the model, and reduce the dependence on a specific scene, so as to reduce false alarms.
2. Methodology
2.1. The YOLOv7 Backbone Network
2.2. EBMA-Net
2.2.1. EMCA Module
UPF Module
DPF Module
ECBT Module
2.2.2. SECA High-Efficiency Dual-Channel Attention Module
2.3. Classifier
3. Experiments
3.1. Experimental Data
3.2. Environment and Parameters
3.3. Assessment of Indicators
3.4. Results
3.4.1. Analysis of Aircraft Detection Results
3.4.2. Aircraft Inspection Performance Assessment
3.4.3. Analysis of Aircraft Identification Results
3.4.4. Performance Evaluation of Aircraft Identification
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Networks | P (%) | R (%) | MDR (%) | FAR (%) |
---|---|---|---|---|
YOLOv5s | 84.2 | 93.68 | 6.32 | 15.8 |
YOLOv7 | 86.78 | 93.78 | 6.22 | 13.22 |
MGCAN | 86.12 | 93.98 | 6.12 | 13.88 |
EBPA2N | 87.31 | 93.88 | 6.22 | 12.69 |
EBMA-Net | 91.31 | 93.88 | 6.12 | 8.69 |
Networks | Type of Aircraft | P (%) | R (%) | MDR (%) | FAR (%) |
---|---|---|---|---|---|
YOLOv5s | Boeing 787 | 93.8 | 92.5 | 7.5 | 6.2 |
A220 | 95.2 | 96 | 4 | 4.8 | |
Boeing 737 | 91.2 | 98.3 | 1.7 | 8.8 | |
Others | 94.3 | 93.9 | 6.1 | 5.7 | |
A320/321 | 91.6 | 1 | 0 | 8.4 | |
ARJ21 | 1 | 95.9 | 4.1 | 0 | |
A330 | 98.4 | 1 | 0 | 1.6 | |
Mean | 94.9 | 96.6 | 3.4 | 5.1 | |
YOLOv7 | Boeing 787 | 93.4 | 96.9 | 3.1 | 6.6 |
A220 | 91.1 | 97.9 | 2.1 | 8.9 | |
Boeing 737 | 85.9 | 1 | 0 | 14.1 | |
Others | 88.3 | 97.1 | 2.1 | 11.7 | |
A320/321 | 90.7 | 1 | 0 | 9.3 | |
ARJ21 | 95.9 | 95.9 | 4.1 | 4.1 | |
A330 | 98.3 | 1 | 0 | 1.7 | |
Mean | 91.9 | 98.2 | 1.8 | 8.1 | |
MGCAN | Boeing 787 | 93.1 | 95.2 | 4.8 | 6.9 |
A220 | 93.3 | 96.9 | 3.1 | 6.7 | |
Boeing 737 | 91.7 | 99.5 | 0.5 | 8.3 | |
Others | 91.8 | 95 | 5 | 8.2 | |
A320/321 | 90.5 | 1 | 0 | 9.5 | |
ARJ21 | 1 | 95.5 | 4.5 | 0 | |
A330 | 98.7 | 1 | 0 | 1.3 | |
Mean | 94.1 | 97.4 | 2.6 | 5.9 | |
EBPA2N | Boeing 787 | 93.8 | 93.6 | 6.7 | 6.2 |
A220 | 94.8 | 95.3 | 4.7 | 5.2 | |
Boeing 737 | 95.4 | 97.2 | 2.3 | 4.6 | |
Others | 93.8 | 93.9 | 6.1 | 6.2 | |
A320/321 | 91.7 | 99.5 | 0.5 | 8.3 | |
ARJ21 | 99.8 | 95.9 | 4.1 | 0.2 | |
A330 | 99.2 | 1 | 0 | 0.8 | |
Mean | 95.5 | 96.5 | 3.5 | 4.5 | |
EBMA-Net | Boeing 787 | 93.8 | 97.4 | 2.6 | 6.2 |
A220 | 94.8 | 97.9 | 2.1 | 5.2 | |
Boeing 737 | 95.2 | 99.8 | 0.2 | 4.8 | |
Others | 93.4 | 97.4 | 3.6 | 6.6 | |
A320/321 | 93.3 | 1 | 0 | 6.7 | |
ARJ21 | 1 | 96.5 | 3.5 | 0 | |
A330 | 99 | 1 | 0 | 1 | |
Mean | 95.6 | 98.4 | 1.6 | 4.4 |
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Wang, J.; Liu, G.; Liu, J.; Dong, W.; Song, W. Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement. Remote Sens. 2024, 16, 3177. https://doi.org/10.3390/rs16173177
Wang J, Liu G, Liu J, Dong W, Song W. Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement. Remote Sensing. 2024; 16(17):3177. https://doi.org/10.3390/rs16173177
Chicago/Turabian StyleWang, Jing, Guohan Liu, Jiaxing Liu, Wenjie Dong, and Wanying Song. 2024. "Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement" Remote Sensing 16, no. 17: 3177. https://doi.org/10.3390/rs16173177