Search Results (8)

In an era where security concerns are ever-increasing, the need for advanced technology to detect visible and concealed weapons has become critical. This paper introduces a novel two-stage method for concealed handgun detection, leveraging thermal imaging and deep learning, offering a potential real-world solution for law enforcement and surveillance applications. The approach first detects potential firearms at the frame level and subsequently verifies their association with a detected person, significantly reducing false positives and false negatives. Alarms are triggered only under specific conditions to ensure accurate and reliable detection, with precautionary alerts raised if no person is detected but a firearm is identified. Key contributions include a lightweight algorithm optimized for low-end embedded devices, making it suitable for wearable and mobile applications, and the creation of a tailored thermal dataset for controlled concealment scenarios. The system is implemented on a chest-worn Android smartphone with a miniature thermal camera, enabling hands-free operation. Experimental results validate the method’s effectiveness, achieving an mAP@50-95 of 64.52% on our dataset, improving state-of-the-art methods. By reducing false negatives and improving reliability, this study offers a scalable, practical solution for security applications. Full article

Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns. Full article

A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s). It is demonstrated that the FPGA data-processing speed matches that of the radar output, thus eliminating data loss. The radar system achieves a remarkable speed of over 9000 waveforms per second on each channel. The proposed architecture is scalable to accommodate higher sampling rates and various waveform periods. It is also multi-functional since the FPGA controls and synchronizes two transmitters and a dual-channel receiver, performs signal reconstruction on both channels simultaneously, and carries out user-defined averaging, trace windowing, and interference suppression for improving the receiver’s signal-to-noise ratio. We also investigate the throughput rate while offloading radar data onto an external device through an Ethernet link. Since the radar data rate significantly exceeds the Ethernet link capacity, we show how the FPGA-based averaging and windowing functions are leveraged to reduce the amount of offloaded data while fully utilizing the radar output. Full article

DEXTER (detection of explosives and firearms to counter terrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. While body scanner systems in secure areas of public places are becoming more and more efficient, the attack at Brussels airport on 22 March 2016, upstream of these systems, in the middle of the crowd of passengers, demonstrated the lack of discreet and real-time security against threats of mass terrorism. The NATO-SPS international and multi-year DEXTER project aims to provide new technical and strategic solutions to fill this gap. This project is based on multi-sensor coordination and fusion, from hyperspectral remote laser to smart glasses, artificial algorithms, and suspect identification and tracking. One of these sensors is dedicated to threat detection (large weapon or explosive belt) using the clothing of pedestrians by means of an active microwave component. This project is referred to as MIC (Microwave Imaging Curtain), also supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real-time to address non-checkpoint detection of explosives and firearms. The project, led by ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra-Wide Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC’s innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar images using a deep-learning network. This paper will outline the project’s objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results obtained during a live demonstration in a relevant environment. Full article

An efficient and more accurate millimeter-wave imaging algorithm, applied to a close-range monostatic personnel screening system, with consideration of dual path propagation loss, is presented in this paper. The algorithm is developed in accordance with a more rigorous physical model for the monostatic system. The physical model treats incident waves and scattered waves as spherical waves with a more rigorous amplitude term as per electromagnetic theory. As a result, the proposed method can achieve a better focusing effect for multiple targets in different range planes. Since the mathematical methods in classical algorithms, such as spherical wave decomposition and Weyl identity, cannot handle the corresponding mathematical model, the proposed algorithm is derived through the method of stationary phase (MSP). The algorithm has been validated by numerical simulations and laboratory experiments. Good performance in terms of computational efficiency and accuracy has been observed. The synthetic reconstruction results show that the proposed algorithm has significant advantages compared with the classical algorithms, and the reconstruction by using full-wave data generated by FEKO further verifies the validity of the proposed algorithm. Finally, the proposed algorithm performs as expected over real data acquired by our laboratory prototype. Full article

Millimeter-wave (MMW) imaging has a tangible prospect in concealed weapon detection for security checks. Typically, a one-dimensional (1D) linear antenna array with mechanical scanning along a perpendicular direction is employed for MMW imaging. To achieve high-resolution imaging, the target under test needs to keep steady enough during the mechanical scanning process since slight movement can induce large phase variation for MMW systems, which will result in a blurred image. However, in the scenario of imaging of a human body, sometimes it is difficult to meet this requirement, especially for the elderly. Such blurred MMW images would reduce the detection accuracy of the concealed weapons. In this paper, we propose a deblurring method based on cycle-consistent adversarial network (Cycle GAN). Specifically, the Cycle GAN can learn the mapping between the blurred MMW images and the focused ones. To minimize the effect of the shaking blur, we introduce an identity loss. Moreover, a mean squared error loss (MSE loss) is utilized to stabilize the training, so as to obtain more refined deblurred results. The experimental results demonstrate that the proposed method can efficiently suppress the blurring effect in the MMW image. Full article

The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data. Full article

In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection. Full article