SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

Han, Ping; Liao, Dayu; Han, Binbin; Cheng, Zheng

doi:10.3390/rs14184669

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…In recent years, high-quality SAR datasets have become increasingly abundant, and researchers have proposed various automatic, fast, and accurate SAR image object detection algorithms [28][29][30]. Traditional SAR image detection methods have predominantly focused on enhancing the well-known Constant False Alarm Rate (CFAR) algorithm.…”

Section: Related Work 21 Object Detection In Sar Imagesmentioning

confidence: 99%

SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain

Luo,

Zhang,

Guo

et al. 2024

Remote Sensing

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The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.

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Section: Related Work 21 Object Detection In Sar Imagesmentioning

confidence: 99%

SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain

Luo,

Zhang,

Guo

et al. 2024

Remote Sensing

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“…Zhang et al introduced the SAR Aircraft Detection Dataset (SADD) and the SEFEPNet, a deep learning model leveraging domain adaptive transfer learning for better detection [3]. Additionally, Han et al's Low-Level Semantic Enhancement Module (LSEM) focuses on enhancing scattered features in SAR imagery [4]. Zhang et al's Fusion Local and Contextual Attention Pyramid (FLCAPN) further advances detection by merging local and contextual features.…”

Section: Introductionmentioning

confidence: 99%

YOLO-SAD: An Efficient SAR Aircraft Detection Network

Chen,

Shen,

Liang

et al. 2024

Applied Sciences

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Aircraft detection in SAR images of airports remains crucial for continuous ground observation and aviation transportation scheduling in all weather conditions, but low resolution and complex scenes pose unique challenges. Existing methods struggle with accuracy, overlapping detections, and missed targets. We propose You Only Look Once-SAR Aircraft Detector (YOLO-SAD), a novel detector that tackles these issues. YOLO-SAD leverages the Attention-Efficient Layer Aggregation Network-Head (A-ELAN-H) module to prioritize essential features for improved accuracy. Additionally, the SAR Aircraft Detection-Feature Pyramid Network (SAD-FPN) optimizes multi-scale feature fusion, boosting detection speed. Finally, Enhanced Non-Maximum Suppression (EH-NMS) eliminates overlapping detections. On the SAR Aircraft Detection Dataset (SADD), YOLO-SAD achieved 91.9% AP(0.5) and 57.1% AP(0.5:0.95), surpassing the baseline by 2.1% and 1.9%, respectively. Extensive comparisons on SADD further demonstrate YOLO-SAD’s superiority over five state-of-the-art methods in both AP(0.5) and AP(0.5:0.95). The outcomes of further comparative experiments on the SAR-AIRcraft-1.0 dataset confirm the robust generalization capability of YOLO-SAD, demonstrating its potential use in aircraft detection with SAR.

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YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images

Wan

Wang

et al. 2023

Remote Sensing

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Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.

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SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

Cited by 3 publications

References 43 publications

SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain

SAR-CDSS: A Semi-Supervised Cross-Domain Object Detection from Optical to SAR Domain

YOLO-SAD: An Efficient SAR Aircraft Detection Network

YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images

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