On improving IED object detection by exploiting scene geometry using stereo processing

Developing automated threat detection algorithms for imaging equipment used by explosive ordnance disposal (EOD) and public safety personnel has the potential to improve mission efficiency and safety by automatically drawing a user’s attention to potential threats. To demonstrate the value of automated threat detection algorithms to the EOD community, Deep Analytics LLC (DA) developed an object detection algorithm that runs in real-time on resource constrained devices. The object detection algorithm identifies 10 common classes of improvised explosive device (IED) components in live video and alerts a user when an IED component is detected. In this paper we discuss the development of the IED component dataset, the training and evaluation of the object detection algorithm, and the deployment on the algorithm on resource constrained hardware.

The detection of person-borne threat objects, such as improvised explosive devices, at a safe distance is an ongoing challenge. While much attention has been given to other parts of the electromagnetic spectrum, very little is known about what potential exists to detect clothing obscured threats over the ultraviolet through the shortwave-infrared spectral region. Hyperspectral imaging may provide a greater ability to discriminate between target and non-target by using the full spectrum. This study investigates this potential by the collection and analysis of hyperspectral images of obscured proxy threat objects. The results of this study indicate a consistent ability to detect the presence of concealed objects. The study included the use of VNIR (400 nm to 1000 nm) and SWIR (1000 nm to 1700 nm), as defined here, hyperspectral imagers. Both spectral ranges provided comparable results, however, potential advantages of the SWIR spectral region are discussed.

A compact single asymmetric coplanar waveguide feed (ACPW‐fed) dual circularly polarized microstrip antenna that operates at 1.8, 3.9, and 5.2 GHz in the entire operating frequency band 600 MHz–6 GHz for the radar detection of the improvised explosive devices (IEDs) carried by a person is introduced. The proposed novel quasi‐omnidirectional antenna consists of single sided rectangular ring microstrip patch antenna. L‐shaped slots are etched at the two opposite corners of the rectangular ring, introducing new resonance and circular polarization waves at the mid and upper bands, respectively. The achieved dual half‐rectangular ring patch antenna (DHRR‐patch) is loaded with strips of various shapes delicately placed at the center of the radiator, providing new resonance at the upper band and the improvement of the CP features. The matching technique designed based on CPW 50 Ω microstrip transmission line combined with the dual broad band matching techniques through quarter‐wave transformer in conjunction with open stubs and distributed lumped element method constitutes the novelty of the study. Based on quasi‐TEM mn (q‐TEM mn ) mode, ACPW‐fed and CP‐slots are employed to generate CP radiations at the q‐TEM 11 and q‐TEM 21 modes, respectively, while the ground plane width is optimized to enhance axial ratio bandwidth (AR‐BW). Input impedance and radiation pattern calculations of the conventional structure using transmission line and cavity model‐based q‐TEM 01 mode are conducted, respectively. Numerical experiments of the studied monolayer antenna are carried out using Advanced Design System (ADS) Version 2009 environment software employing internal one‐port option to excite the antenna. The prototype of the proposed antenna with a compact dimension (0.27 λ g × 0.38 λ g × 0.02 λ g at 1.8 GHz where λ g is the guided wavelength of the q‐TEM 01 mode) is fabricated on high loss laminate FR4 substrate of volume 43 × 38 × 1.6 (mm 3 ) and relative dielectric constant of 4.4 with simple laboratory‐based traditional printed circuit board (PCB) etching process. Measurement results show a fractional impedance bandwidth (FIBW) of 11.1%, 5.9%, and 7.1%, axial ratio (AR) of 4.6, 2.2, and 0.5 dB, and peak gain of 3.7, 4.7, and 6.1 dBic at 1.8, 4.0, and 5.2 GHz, respectively, demonstrating its suitability for IED detection applications. To verify the efficiency of the proposed model, measured results are compared with the simulated results and good agreement has been established.

Automatic detection of occluded targets from a sequence of images is an interesting area of research for defense related application. In this paper, change detection methods are investigated for the detection of buried improvised explosive devices (IED) using temporal thermal hyperspectral scenes. Specifically, the paper assesses the detection of buried small aluminium plates using the TELOPS Hyper-Cam sensor and by applying two change detection algorithms: multivariate statistical based method (Cross-Covariance (CC)) and class-conditional change detector (QCC). It was found that spectral based change detection is a good method for the detection of buried IED under disturbed soil. Moreover, the Cross-Covariance (CC)) and the class-conditional (QCC) change detector were able to detect changes using short temporal sequences as long temporal sequences pairs.

Countless applications today are using mobile robots, including autonomous navigation, security patrolling, housework, search-and-rescue operations, material handling, manufacturing, and automated transportation systems. Regardless of the application, a mobile robot must use a robust autonomous navigation system. Autonomous navigation remains one of the primary challenges in the mobile-robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. Among autonomous navigation methods, vision-based systems have been growing in recent years due to rapid gains in computational power and the reliability of visual sensors. The primary focus of research into vision-based navigation is to allow a mobile robot to navigate in an unstructured environment without collision. In recent years, several researchers have looked at methods for setting up autonomous mobile robots for navigational tasks. Among these methods, stereovision-based navigation is a promising approach for reliable and efficient navigation. In this article, we create and develop a novel mapping system for a robust autonomous navigation system. The main contribution of this article is the fuse of the multi-baseline stereovision (narrow and wide baselines) and laser-range reading data to enhance the accuracy of the point cloud, to reduce the ambiguity of correspondence matching, and to extend the field of view of the proposed mapping system to 180°. Another contribution is the pruning the region of interest of the three-dimensional point clouds to reduce the computational burden involved in the stereo process. Therefore, we called the proposed system multi-sensors multi-baseline mapping system. The experimental results illustrate the robustness and accuracy of the proposed system.

IoT Enabled Automated Object Recognition for the Visually Impaired

The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

In this paper, we present a system for automatic object detection and pose estimation from a single depth map containing multiple objects for bin-picking applications. The proposed object detection algorithm is based on matching the keypoints extracted from the depth image by using the RANSAC algorithm with the spin image descriptor. In the proposed system, we combine the keypoint detection and the RANSAC algorithm to detect the objects, followed by the ICP algorithm to refine the 3D pose estimation. In addition, we implement the proposed algorithm on the GPGPU platform to speed-up the computation. Experimental results on simulated depth data are shown to demonstrate the proposed system.

Object Detection Technology has been a subject to much research and development due to increasing use of images and videos as data sources and their huge number of applications. Traditional models for object detection had limitations in training and did not use transfer learning for their benefit. With the evolution of deep learning and Neural networks, newer and powerful tools have made way to achieve Object Detection in real-time, with the added advantage of transfer learning and detection of multiple instances of different classes of interest in the given image context. The proposed system is an Object Detection model based on the Single Shot Detector (SSD) algorithm trained with MobileNetV2 feature extraction that can be utilized and integrated in e-commerce, hospitality industry, security and surveillance, real estate, self-driving cars and floor inventory management.

In this paper, we propose a practical method for eye blink detection using a monocular system. Eye blink detection is an important technology in many situations such as facial action analysis and signal processing. However, automatic eye blink detection is quite difficult since the eye blink is occurred fast. Fast head tracking and eye detection methods are therefore required in real environment. The proposed method is based on the facial feature point tracking algorithm by using particle filtering. The proposed method works at over 60fps and is able to detect accurate blink motion in real time although our system require only one USB camera. In order to confirm the effectiveness, we show some of applications using our blink detection system.

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects.

Purpose. This article proposes a new control monitoring grid connected hybrid system. The proposed system, automatic detection or monitoring of fault occurrence in the photovoltaic application is extremely mandatory in the recent days since the system gets severely damaged by the occurrence of different faults, which in turn results in performance degradation and malfunctioning of the system. The novelty of the proposed work consists in presenting solar power monitoring and power control based Internet of things algorithm. In consideration to this viewpoint, the present study proposes the Internet of Things (IoT) based automatic fault detection approach, which is highly beneficial in preventing the system damage since it is capable enough to identify the emergence of fault on time without any complexities to generate Dc voltage and maintain the constant voltage for grid connected hybrid system. Methods. The proposed DC-DC Boost converter is employed in this system to maximize the photovoltaic output in an efficient manner whereas the Perturb and Observe algorithm is implemented to accomplish the process of maximum power point tracking irrespective of the changes in the climatic conditions and then the Arduino microcontroller is employed to analyse the faults in the system through different sensors. Eventually, the IoT based monitoring using fuzzy nonlinear autoregressive exogenous approach is implemented for classifying the faults in an efficient manner to provide accurate solution of fault occurrence for preventing the system from failure or damage. Results. The results obtained clearly show that the power quality issue, the proposed system to overcome through monitoring of fault solar panel and improving of power quality. The obtained output from the hybrid system is fed to the grid through a 3ϕ voltage source inverter is more reliable and maintained power quality. The power obtained from the entire hybrid setup is measured by the sensor present in the IoT based module. The experimental validation is carried out in ATmega328P based Arduino UNO for validating the present system in an efficient manner. Originality. The automatic Fault detection and monitoring of solar photovoltaic system and compensation of grid stability in distribution network based IoT approach is utilized along with sensor controller. Practical value. The work concerns a network comprising of electronic embedded devices, physical objects, network connections, and sensors enabling the sensing, analysis, and exchange of data. It tracks and manages network statistics for safe and efficient power delivery. The study is validated by the simulation results based on real interfacing and real time implementation.

In augmented reality (AR), audio markers can be alternatives to image markers for rendering virtual objects when an AR device camera fails to identify the image marker due to lighting conditions and/or the distance between the marker and device. However, conventional audio markers simply broadcast a rendering queue to anonymous devices, making it difficult to provide specific virtual objects of interest to the user. To overcome this limitation without relying on camera-based sensing, we propose a user-aware audio marker system using low frequency ultrasonic signal processing. The proposed system detects users who stay within the marker using ultrasonic-based object detection, and then it uses ultrasonic communication based on windowed differential phase shift keying modulation in order to send a rendering queue only to those users near the marker. Since the proposed system uses commercial microphones and speakers, conventional telecommunication systems can be employed to deliver the audio markers. The performance of the proposed audio marker system is evaluated in terms of object detection accuracy and communication robustness. First, the object detection accuracy of the proposed system is compared with that of a pyroelectric infrared (PIR) sensor-based system in indoor environments, and it is shown that the proposed system achieves a lower equal error rate than the PIR sensor-based system. Next, the successful transmission rate of the proposed system is measured for various distances and azimuths under noisy conditions, and it is also shown that the proposed audio marker system can successfully operate up to approximately 4 m without any transmission errors, even with 70 dBSPL ambient noise.

In this work, the wireless power transfer system employing nested open loop ring resonators at the ground plane is proposed. In the suggested design both the transmitter and the receiver are symmetric. The forward-facing view contains the transmission line. The ground plane contains number of 33 open loop ring resonators which are nested together. The system has a size of 40 × 40 mm2 with a transmission distance of 15 mm. The proposed design is investigated in two cases with and without the loading capacitor. Comparisons between the resonance frequency and the power transfer efficiency of the proposed design with and without the loading capacitor are presented. The coupled resonator system achieved an efficiency of 97% at 1.35 GHz while the system with the loading capacitor achieved an efficiency of 98.8% at 1 GHz. The proposed system is designed, simulated, and measured.

Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach