A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement

HAN Ping; ZHAO Han; LIAO Dayu; PENG Yanwen; CHENG Zheng

doi:10.11999/JEIT240491

Volume 46 Issue 12

Dec. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(12): 4459-4470

HAN Ping, ZHAO Han, LIAO Dayu, PENG Yanwen, CHENG Zheng. A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4459-4470. doi: 10.11999/JEIT240491

Citation:

HAN Ping, ZHAO Han, LIAO Dayu, PENG Yanwen, CHENG Zheng. A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4459-4470. doi: 10.11999/JEIT240491

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A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement

doi: 10.11999/JEIT240491 cstr: 32379.14.JEIT240491

Tianjin Key Laboratory of Intelligent Signal and Image Processing, Civil Aviation University of China, Tianjin 300300, China

Funds: The Central University Fund (3122020043)

Received Date: 2024-06-14
Rev Recd Date: 2024-11-21

Available Online: 2024-11-25

Publish Date: 2024-12-01

Abstract

Abstract

In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
- Synthetic Aperture Radar(SAR),
- Aircraft target detection and recognition,
- YOLOv5s,
- Aircraft feature protection,
- Target area feature enhancement

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References(24)

References

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