CNN with spatio-temporal information for fast suspicious object detection and recognition in THz security images

Yang, Xi; Wu, Tan Chen; Zhang, Lei; Dong, Yuan; Wang, Nannan; Song, Bin; Gao, Xinbo

doi:10.1016/j.sigpro.2019.02.029

Cited by 43 publications

(17 citation statements)

References 23 publications

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“…[45], [46]. Works related to deep learning networks [47]- [50] have also been carried out in recent past. Although most of the work has shown that CNN is a very useful approach in mmW object detection, almost all of the work available in the literature focuses solely on the learning algorithm and not on the data collection for training.…”

Section: Related Workmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

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Section: Related Workmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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“…Zhang et al [43] adopted a faster region-based convolutional neural network (Faster R-CNN) to detect concealed weapons carried on personnel and achieved an encouraging result. Yang et al [44] improved the effectiveness and efficiency of the concealed objects' detection by taking two measures, firstly, they employed a deeper CNN model (VGG-16) for THz images classification. Secondly, they utilized the spatio-temporal information via a sparse and low-rank decomposition model.…”

Section: Convolutional Neural Network and Transfer Learningmentioning

confidence: 99%

A Deep Transfer Learning Model for Packaged Integrated Circuit Failure Detection by Terahertz Imaging

Mao

Liu

2021

IEEE Access

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“…The algorithm achieved better performance on both precision and recall. In addition, the algorithms proposed in literature [ 44 , 45 ] are dedicated to locating and classifying the hidden objects in the MMW human images. Lei Pang et al [ 46 ] introduced the YOLOv3 algorithm into concealed object detection, which is an one-step detection algorithm, and real time and high accuracy detection is realized.…”

Section: Related Workmentioning

confidence: 99%

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

Meng

Zhang

Wang

2020

Sensors

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Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount and complete sample sets of objects. In this paper, a new algorithm is developed using the human pose segmentation followed by the deep CNN detection. The algorithm is emphasized to learn the similarity with humans’ body clutter applied to training corresponding CNNs after the image segmentation base of the pose estimation. Moreover, the suspicious object recognition in the MMW image is converted to a binary classification task. Instead of recognizing all sorts of suspicious objects, the CNN detector determines whether the body part images present the abnormal patterns containing suspicious objects. The proposed algorithm that is based on CNN with the pose segmentation has concise configuration, but optimal performance in the suspicious object detection. Extensive experiments confirm the effectiveness and superiority of the proposal.

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CNN with spatio-temporal information for fast suspicious object detection and recognition in THz security images

Cited by 43 publications

References 23 publications

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

A Deep Transfer Learning Model for Packaged Integrated Circuit Failure Detection by Terahertz Imaging

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

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