A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Yuan, Ming; Zhang, Quansheng; Li, Yinwei; Yan, Yonghong; Zhu, Yiming

doi:10.3390/rs13244978

Cited by 16 publications

(3 citation statements)

References 28 publications

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“…The detection module fused low-level and high-level features, and the domain adaptive module extracted the domain differences of features. Yuan et al [40] proposed a multi-path feature pyramid (MPFP) model and an improved residual block distribution to enhance the recognition accuracy. Wang et al [41] proposed a concealed object detection method based on normalized accumulation maps (NAM), which can reveal frequently occurring concealed object locations.…”

Section: Concealed Object Detection On Millimeter Wave Imagesmentioning

confidence: 99%

Swin-YOLO for Concealed Object Detection in Millimeter Wave Images

Huang,

Wei,

et al. 2023

Applied Sciences

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Concealed object detection in millimeter wave (MMW) images has gained significant attention in the realm of public safety, primarily due to its distinctive advantages of non-hazardous and non-contact operation. However, this undertaking confronts substantial challenges in practical applications, owing to the inherent limitations of low imaging resolution, small concealed object size, intricate environmental noise, and the need for real-time performance. In this study, we propose Swin-YOLO, an innovative single-stage detection model built upon transformer layers. Our approach encompasses several key contributions. Firstly, the integration of Local Perception Swin Transform Layers (LPST Layers) enhanced the network’s capability to acquire contextual information and local awareness. Secondly, we introduced a novel feature fusion layer and a specialized prediction head for detecting small targets, effectively leveraging the network’s shallow feature information. Lastly, a coordinate attention (CA) module was seamlessly incorporated between the neck network and the detection head, augmenting the network’s sensitivity towards critical regions of small objects. To validate the efficacy and feasibility of our proposed method, we created a new MMW dataset containing a large number of small concealed objects and conducted comprehensive experiments to evaluate the effectiveness of overall and partial improvements, as well as computational efficiency. The results demonstrated a remarkable 4.7% improvement in the mean Average Precision (mAP) for Swin-YOLO compared with the YOLOv5 baseline. Moreover, when compared with other enhanced transformer-based models, Swin-YOLO exhibited a superior accuracy and the fastest inference speed. The proposed model showcases enhanced performance and holds promise for advancing the capabilities of real-world applications in public safety domains.

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Section: Concealed Object Detection On Millimeter Wave Imagesmentioning

confidence: 99%

Swin-YOLO for Concealed Object Detection in Millimeter Wave Images

Huang,

Wei,

et al. 2023

Applied Sciences

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“…However, it cannot consider the multiple scatterings around arms and legs. Multipath artifacts from high-order scattering occur in reconstructed images Yuan et al [9]; Meng et al [10], which has a negative impact on the detection of hidden objects in practical security inspection applications. Performing specific postures such as spreading legs and raising hands are useful to reduce multipath artifacts.…”

Section: Introductionmentioning

confidence: 99%

Artifact suppression using cross-circular polarization for millimeter-wave imaging

Qiu

Zhang

et al. 2023

Front. Phys.

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Millimeter-wave imaging is widely applied to radar detection, personnel security inspection, environmental perception, and so on. Multipath artifact is a common phenomenon in active millimeter-wave (AMMW) imaging and is difficult to be removed. The suppression methods in image reconstruction and post-processing stage are usually complex or time-consuming. This report proposes a cross-polarization method to suppress multipath artifacts between equivalent two-cylinder structures before image reconstruction. The physical mechanisms of co-polarization and cross-polarization imaging are revealed. The suppression effectiveness is demonstrated by conducting W-band AMMW imaging simulation and measurement experiments of a typical multipath scenario at the central frequency of 100 GHz (97 ∼103 GHz). The proposed method can directly reconstruct the image with artifact suppression and reduce the pressure of post-processing.

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“…It is also difficult to effectively ensure location accuracy [4]. Compared with the single-view SAR location technology, the multi-view SAR target location technology has more obvious advantages in location accuracy and can theoretically reduce the errors caused by external factors in the process of single-view location [5]. The first problem to be solved in multi-view SAR target location is how to determine the same target in the SAR images with different viewing angles.…”

Section: Introductionmentioning

confidence: 99%

A Fast Matching Method for the SAR Images with Large Viewing Angles Based on Inertial Navigation Information and Neighborhood Structure Consensus

Yan,

Zhao,

et al. 2023

Remote Sensing

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In the field of multi-view SAR target location, the greater the difference in viewing angles, the higher the target location accuracy. However, this makes it difficult to match the same target between the SAR images with different viewing angles. The performance of traditional SAR image-matching algorithms will deteriorate or even fail to match the images correctly when the viewing angle is gradually increased. To solve this problem, a fast SAR matching method for the SAR images with large viewing angles based on inertial navigation information and neighborhood structure consensus (ININSC) is proposed in this paper. In this algorithm, the key targets are detected in the SAR images by using the maximum connected domain algorithm and the K-means clustering algorithm, and the connected domain centroid of the target is taken as a feature point. The approximate position of the key targets after the viewing angle change is found through inertial navigation information, and then accurate and fast matching is achieved by using the consensus of the neighborhood topological structure of the key targets. The measured data sets demonstrate that compared with traditional SAR image-matching algorithms, the proposed ININSC algorithm solves such a problem that SAR images cannot be accurately matched under the differences at large viewing angles and has better robustness and timeliness.

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A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Cited by 16 publications

References 28 publications

Swin-YOLO for Concealed Object Detection in Millimeter Wave Images

Swin-YOLO for Concealed Object Detection in Millimeter Wave Images

Artifact suppression using cross-circular polarization for millimeter-wave imaging

A Fast Matching Method for the SAR Images with Large Viewing Angles Based on Inertial Navigation Information and Neighborhood Structure Consensus

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