Vehicle Target Detection Method for Wide-Area SAR Images Based on Coarse-Grained Judgment and Fine-Grained Detection

The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 13; p. 3242
Main Authors Song, Yucheng, Wang, Shuo, Li, Qing, Mu, Hongbin, Feng, Ruyi, Tian, Tian, Tian, Jinwen
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Accuracy
Algorithms
Analysis
Artificial satellites in remote sensing
Deep learning
Health aspects
Image enhancement
Image processing
Integrated approach
Methods
Military technology
Neural networks
Noise
Processing speed
Radar imaging
Real time
Reconnaissance
Remote sensing
remote sensing images
SAR imagery
Satellites
Synthetic aperture radar
Target detection
vehicle detection
Vehicles
China
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Summary:The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and achieving high accuracy. In this study, we address the challenges of detecting vehicle targets in wide-area SAR images and propose a novel method that combines coarse-grained judgment with fine-grained detection to overcome these challenges. Our proposed vehicle detection model is based on YOLOv5, featuring a CAM attention module, CAM-FPN network, and decoupled detection head, and it is strengthened with background-assisted supervision and coarse-grained judgment. These various techniques not only improve the accuracy of the detection algorithms, but also enhance SAR image processing speed. We evaluate the performance of our model using the Wide-area SAR Vehicle Detection (WSVD) dataset. The results demonstrate that the proposed method achieves a high level of accuracy in identifying vehicle targets in wide-area SAR images. Our method effectively addresses the challenges of detecting vehicle targets in wide-area SAR images, and has the potential to significantly enhance real-time reconnaissance tasks and promote the widespread application of remote sensing technology in various fields.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15133242