Blind Detection Research Articles

Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Simulated Vehicle-to-Pedestrian Collisions

This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible.

Driver Safety System

Abstract: Blind spots in vehicles and driver drowsiness are significant safety concerns that contribute to road accidents. To address these issues, we propose a comprehensive driver drowsiness and blind spot detection system using advanced technologies and image processing algorithms. The blind spot detection system employs ultrasonic sensors and an Arduino microcontroller board to gather real-time information about potential collision objects in the blind spot area. When a risk is detected, an LED alarm alerts the driver, enhancing their awareness and reducing the likelihood of accidents. Additionally, we explore the implementation of a camera-based blind spot detection system using deep learning techniques, offering a viable option for autonomous vehicles. For driver drowsiness detection, we present a non-intrusive method using a camera to continuously monitor the driver's facial features, such as eye and head movements. By analyzing these features, the system can accurately identify three driver states: awake, drowsy, and sleeping. When drowsiness is detected, the system activates alerts, such as visual and auditory cues, to awaken the driver and ensure their safety. The literature review greatly impacted our system's design, facilitating informed decisions and integration of cutting-edge technologies for an effective driver drowsiness and blind spot detection solution. The research paper also discusses the integration of these systems into various platforms, including mobile apps, Python programs, and IP cameras, providing flexible and cost-effective solutions for different vehicle types. Results from prototype implementations demonstrate the effectiveness and reliability of the proposed methods in detecting blind spots and drowsiness. Overall, this research enhances road safety via efficient blind spot & drowsiness detection, saving lives by addressing driver fatigue. Utilizing tech & machine learning, proposed systems advance active safety & driver assistance

Applying Image Analysis to Build a Lightweight System for Blind Obstacles Detecting of Intelligent Wheelchairs

Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle detection model, named GC-YOLO, and built an obstacle dataset that consists of incomplete target images captured in the blind spot view of the smart wheelchair. The feature extraction operations are simplified in the backbone and neck sections of GC-YOLO. The backbone network uses GhostConv in the GhostNet network to replace the ordinary convolution in the original feature extraction network, reducing the model size. Meanwhile, the CoordAttention is applied, aiming to reduce the loss of location information caused by GhostConv. Further, the neck stem section uses a combination module of the lighter SE Attention module and the GhostConv module to enhance the feature extraction capability. The experimental results show that the proposed GC-YOLO outperforms the YOLO5 in terms of model parameters, GFLOPS and F1. Compared with the YOLO5, the number of model parameters and GFLOPS are reduced by 38% and 49.7%, respectively. Additionally, the F1 of the proposed GC-YOLO is improved by 10% on the PASCAL VOC dataset. Moreover, the proposed GC-YOLO achieved mAP of 90% on the custom dataset.

Design and Performance Analysis of Hardware Realization of 3GPP Physical Layer for 5G Cell Search

5G Cell Search (CS) is the first step for user equipment (UE) to initiate communication with the 5G node B (gNB) every time it is powered ON. In cellular networks, CS is accomplished via synchronization signals (SS) broadcasted by gNB. 5G 3rd generation partnership project (3GPP) specifications offer a detailed discussion on the SS generation at gNB, but a limited understanding of their blind search and detection is available. Unlike 4G, 5G SS may not be transmitted at the center of carrier frequency, and their frequency location is unknown to UE. In this work, we demonstrate the 5G CS by designing 3GPP compatible hardware realization of the physical layer (PHY) of the gNB transmitter and UE receiver. The proposed SS detection explores a novel down-sampling approach resulting in a 60% reduction in on-chip memory and 50% lower search time. Via detailed performance analysis, we analyze the functional correctness, computational complexity, and latency of the proposed approach for different word lengths, signal-to-noise ratio (SNR), and down-sampling factors. We demonstrate end-to-end 5G CS using GNU Radio-based RFNoC framework on the USRP-FPGA platform and achieve 66% faster SS search compared to software. The 3GPP compatibility and demonstration on hardware strengthen the commercial significance of the proposed work.

A Dual Blind Watermarking Method for 3D Models Based on Normal Features.

Digital watermarking technology is an important means to effectively protect three-dimensional (3D) model data. Among them, "blind detection" and "robustness" are key and difficult points in the current research of digital watermarking technology based on 3D models. In order to realize the blind detection of a watermark and improve its robustness against various common attacks at the same time, this paper proposes a dual blind watermarking method based on the normal feature of the centroid of first-ring neighboring points. The local spherical coordinate system is constructed by calculating two different normal vectors, and the first pattern watermark and the second random binary sequence watermark are embedded, respectively. The experimental results show that this method can not only realize the blind detection of dual watermarks, but also have the ability to resist common attacks such as translation, rotation, scaling, cropping, simplification, smoothing, noise, and vertex reordering to a certain extent.

MXene‐Composite‐Enabled Ultra‐long‐Distance Detection and Highly Sensitive Self‐Powered Noncontact Triboelectric Sensors and Their Applications in Intelligent Vehicle Perception

AbstractWith the ongoing advancement in sensor technologies, the development of intelligent, secure, and low‐carbon vehicles will be possible in the near future. However, providing continuous power to numerous onboard sensors consumes substantial amounts of power from vehicle, resulting in the shortened running times. Herein, a self‐powered noncontact triboelectric nanogenerator (SNC‐TENG) with ultralong‐distance detection and a high sensitivity is proposed, which can be used as ambient perception sensors for smart vehicles. The SNC‐TENG is constructed from a MXene/silicone nanocomposite with a fully embedded conductive sponge, which significantly enriches the tribo‐charges and leads to an excellent capability and performance. This SNC‐TENG is capable of perceiving human activity from a distance of 2 m. To the best of authors knowledge, the perceiving distance per unit area of this SNC‐TENG is the highest among previously reported non‐contact TENGs. Furthermore, the applicability of the SNC‐TENG is explored for vehicle sentry mode and blind spot detection for the first time. The use of this technology in a real vehicle demonstrates its feasibility for use in practical applications. Overall, this study provides a new design scheme for contactless self‐powered sensors, and the presented SNC‐TENG is expected to be applicable in various fields where non‐contact sensing is required.

Efficient Real-Time Whitening for Blind Eigenvalue-Based Detection in mmWave Full Duplex Cognitive Radio

Although combining Full Duplex (FD) and Cognitive Radio (CR) technologies can provide several benefits, the presence of the residual self-interference (RSI) at the FD transceiver may influence the spectrum sensing capabilities. In particular, focusing on the blind eigenvalue-based detectors (EVDs), this paper shows that operating at mmWave widebands, the RSI is a colored noise strongly affecting the detection performance even when it is characterized by low power. Consequently, we propose here a whitened detector whose novelty consists in the real-time computation of the whitening matrix, differently from previous works which employ an offline computation. The proposed whitening matrix computation uses a sample covariance matrix (SCM) regularization tailored for the FD scenario, instead of the conventional SCM estimator. Particularly, we prove that the proposed whitened detector is robust against RSI power uncertainty, attaining a constant false alarm rate under typical FD operating conditions. Finally, we show that our method provides computational load reduction and faster convergence to the theoretical false alarm probability characteristic of the white noise case. Numerical simulations validate the theoretical analysis confirming the better performance of the proposed whitening approach, particularly when applied to the sphericity test, in comparison to the offline-based approach.

Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM

Audio recordings used as evidence have become increasingly important to litigation. Before their admissibility as evidence, an audio forensic expert is often required to help determine whether the submitted audio recordings are altered or authentic. Within this field, the copy-move forgery detection (CMFD), which focuses on finding possible forgeries that are derived from the same audio recording, has been an urgent problem in blind audio forensics. However, most of the existing methods require idealistic pre-segmentation and artificial threshold selection to calculate the similarity between segments, which may result in serious misleading and misjudgment especially on high frequency words. In this work, we present a robust method for detecting and locating an audio copy-move forgery on the basis of constant Q spectral sketches (CQSS) and the integration of a customised genetic algorithm (GA) and support vector machine (SVM). Specifically, the CQSS features are first extracted by averaging the logarithm of the squared-magnitude constant Q transform. Then, the CQSS feature set is automatically optimised by a customised GA combined with SVM to obtain the best feature subset and classification model at the same time. Finally, the integrated method, named CQSS-GA-SVM, is evaluated against the state-of-the-art approaches to blind detection of copy-move forgeries on real-world copy-move datasets with read English and Chinese corpus, respectively. The experimental results demonstrate that the proposed CQSS-GA-SVM exhibits significantly high robustness against post-processing based anti-forensics attacks and adaptability to the changes of the duplicated segment duration, the training set size, the recording length, and the forgery type, which may be beneficial to improving the work efficiency of audio forensic experts.

A Novel Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication

A Novel Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication

Effects of Advanced Driver Assistance Systems on Impact Velocity and Injury Severity: An Exploration of Data from the Crash Investigation Sampling System

This paper presents an exploratory analysis of advanced driver assistance systems (ADAS) features and safety outcomes using National Highway Transportation Safety Administration’s Crash Investigation Sampling System (CISS) database. In this database, we found improved safety outcomes associated with the presence of three ADAS features: lane departure warning, forward collision warning, and blind spot detection. In particular, vehicles with ADAS features were less likely to be involved in fatal and severe crashes than vehicles of a similar age and body style. Additionally, vehicles with ADAS features had a lower rate of change in velocity pre- and postcrash, a key proxy for injury severity. Vehicles with ADAS were also less likely to be involved in head-on and rear-end crashes than similar vehicles without ADAS and were less likely to experience a severe outcome than vehicles without ADAS when involved in sideswipe collisions. Despite these promising findings, however, small sample sizes precluded robust within-category and multivariate analysis. More robust data are urgently needed to disentangle the safety effects of ADAS features from those related to confounding variables such as vehicle age and body type for vehicle occupants, pedestrians, and bicyclists. We conclude the paper with recommendations for improvements to the CISS database that could support future investigations. We hope this work provides a baseline for scholars and practitioners to deepen our understanding of the effects of ADAS features on transportation safety.

A novel blind tamper detection and localization scheme for multiple faces in digital images

AbstractFace image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.

An Effective YOLO-Based Proactive Blind Spot Warning System for Motorcycles

Interest in advanced driver assistance systems (ADAS) is booming in recent years. One of the most effervescent ADAS features is blind spot detection (BSD), which uses radar sensors or cameras to detect vehicles in the blind spot area and alerts the driver to avoid a collision when changing lanes. However, this kind of BSD system fails to notify nearby vehicle drivers in this blind spot of the possible collision. The goal of this research is to design a proactive bus blind spot warning (PBSW) system that will immediately notify motorcyclists when they enter the blind spot or the area of the inner wheel difference of a target vehicle, i.e., a bus. This will increase the real-time functionality of BSD and can have a significant impact on enhancing motorcyclist safety. The proposed hardware is placed on the motorcycle and consists of a Raspberry Pi 3B+ and a dual-lens stereo camera. We use dual-lens cameras to capture and create stereoscopic images then transmit the images from the Raspberry Pi 3B+ to an Android phone via Wi-Fi and to a cloud server using a cellular network. At the cloud server, we use the YOLOv4 image recognition model to identify the position of the rear-view mirror of the bus and use the lens imaging principle to estimate the distance between the bus and the motorcyclist. Finally, the cloud server returns the estimated distance to the PBSW app on the Android phone. According to the received distance value, the app will display the visible area/blind spot, the area of the inner wheel difference of the bus, the position of the motorcyclist, and the estimated distance between the motorcycle and the bus. Hence, as soon as the motorcyclist enters the blind spot of the bus or the area of the inner wheel difference, the app will alert the motorcyclist immediately to enhance their real-time safety. We have evaluated this PBSW system implemented in real life. The results show that the average position accuracy of the rear-view mirror is 92.82%, the error rate of the estimated distance between the rear-view mirror and the dual-lens camera is lower than 0.2%, and the average round trip delay between the Android phone and the cloud server is about 0.5 s. To the best of our knowledge, this proposed system is one of few PBSW systems which can be applied in the real world to protect motorcyclists from the danger of entering the blind spot and the area of the inner wheel difference of the target vehicle in real time.

Nondestructive millimeter-scale void detection for thick steel-shell–concrete interface of immersed tube tunnel: case study

The structural form of sandwich-structured immersed tunnel (SSIT) can be complex. During the casting of self-compacting concrete, creating void defects between the steel shell and concrete interface is not difficult, which can adversely affect the overall safety and service life of the structure. However, detecting millimeter-scale voids covered by a thick steel plate is a technical challenge for current engineering industries. In this study, we proposed a nondestructive millimeter-scale void detection method for SSITs with thick steel shells by combining impact imaging and neutron methods. First, based on the near-source wavefield theory and count rate of thermal neutrons, the void area and depth calculation methods were derived theoretically, and then the coupling detection method and grading criteria for void severity were proposed. Additionally, the void detection performance was validated for a full-scale SSIT model test by blind detection. Finally, the proposed method was applied to the SSIT of the Shenzhen–Zhongshan bridge. The results showed that the proposed method could quantitatively determine the location and distribution pattern of a void; however, it could not accurately determine the void depth. In contrast, the neutron method could accurately calculate the void depth but had a large minimum detectable unit area. The proposed method could effectively compensate for the limitations of both methods. Statistically, the coincidence rate of the model test was 95%, 89%, and 87.5% for the void location, void area, and void depth, respectively, when the error range was ±2 mm. Using this method, 30 tubes in the Shenzhen–Zhongshan bridge were inspected, and by summarizing the void law, suggestions to improve the casting process were proposed, such as adjusting the casting speed. Meanwhile, the void probability decreased significantly. The proposed method provides an important basis for high-quality construction in SSIT projects.

Distributed Space Shift Keying in Cooperative Wireless Networks: Capacity Analysis and Error Performance

In this study, a distributed fashion of space shift keying as an efficient cooperative framework is proposed and studied to address the main challenges in future cellular networks by providing high energy efficiency and meeting the demand growth with the lowest complexity and the highest performance. A comprehensive mathematical analysis has been performed to find a closed form for the channel capacity of distributed space shift keying and to depict the influence of the number of distributed antennas on the achievable channel capacity. In addition, a simulation study of the error performance is presented using two detection methods: maximum-likelihood detection based on channel state information and blind detection. It was shown that as the number of cooperating relays increases, the error performance of the detectors converges, although they incur different costs and computational complexities. A comparison study in terms of error performance and capacity is applied with other cooperative spatial modulation to portray the outperformance of the system presented in this work.

Measuring the X-ray luminosities of DESI groups from eROSITA Final Equatorial-Depth Survey – I. X-ray luminosity–halo mass scaling relation

ABSTRACT We use the eROSITA Final Equatorial-Depth Survey (eFEDS) to measure the rest-frame 0.1–2.4 keV band X-ray luminosities of ∼600 000 DESI groups using two different algorithms in the overlap region of the two observations. These groups span a large redshift range of 0.0 ≤ zg ≤ 1.0 and group mass range of $10^{10.76}\, h^{-1}\, \mathrm{M}_{\odot } \le M_h \le 10^{15.0}\, h^{-1}\, \mathrm{M}_{\odot }$. (1) Using the blind detection pipeline of eFEDS, we find that 10932 X-ray emission peaks can be cross-matched with our groups, ∼38 per cent of which have a signal-to-noise ratio $\rm {S}/\rm {N} \ge 3$ in X-ray detection. Comparing to the numbers reported in previous studies, this matched sample size is a factor of ∼6 larger. (2) By stacking X-ray maps around groups with similar masses and redshifts, we measure the average X-ray luminosity of groups as a function of halo mass in five redshift bins. We find that in a wide halo mass range, the X-ray luminosity, LX, is roughly linearly proportional to Mh and quite independent to the redshift of the groups. (3) We use a Poisson distribution to model the X-ray luminosities obtained using two different algorithms and obtain the best-fit $L_{\rm X}=10^{28.46\pm 0.03}M_{\rm h}^{1.024\pm 0.002}$ and $L_{\rm X}=10^{26.73 \pm 0.04}M_{\rm h}^{1.140 \pm 0.003}$ scaling relations, respectively. The best-fit slopes are flatter than the results previously obtained but closer to a self-similar prediction.

Commissioning of a hadron blind detector for dielectron measurement in pA reactions at J-PARC

The J-PARC E16 experiment measures mass spectra of low-mass vector mesons, ρ, ω, and ϕ, in nuclei via e + e - decays. Vector mesons are produced in pA reactions. A spectrometer with a large acceptance and a high-intensity beam up to 1 × 1010 protons per ∼2-sec duration pulse are utilized to collect a sufficient number of vector mesons. We have developed a spectrometer, which has two types of electron identification detectors: a hadron blind detector (HBD) and a lead-glass calorimeter. Commissioning runs have been performed at the J-PARC high-momentum beam line. The HBD successfully observed on average 11 ± 1 photoelectrons for an incident electron. We achieved the pion rejection of 0.9 ± 0.2% with the electron detection efficiency of 61 ± 4% for the HBD, which was consistent of the expected performance.

Comparative analysis of blind detectors in a cluster-based cooperative Spectrum Hole detection

Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate.

The Different Welding Layers and Heat Source Energy on Residual Stresses in Swing Arc Narrow Gap MAG Welding.

In this paper, in order to reduce the time cost of prediction experiments in industry, a new narrow gap oscillation calculation method is developed in ABAQUS thermomechanical coupling analysis to study the distribution trend of residual weld stresses in comparison with conventional multi-layer welding processes. The blind hole detection technique and thermocouple measurement method verify the reliability of the prediction experiment. The results show that the experimental and simulation results have a high degree of agreement. In the prediction experiments, the calculation time of the high-energy single-layer welding experiments is 1/4 of the traditional multi-layer welding. Two welding processes of longitudinal residual stress and transverse residual stress distribution trends are the same. The high-energy single-layer welding experiment stress distribution range and transverse residual stress peak are smaller, but the longitudinal residual stress peak is slightly higher, which can be effectively reduced by increasing the preheating temperature of the welded parts. This implies that in the specific case of increasing the initial temperature of the workpiece, the use of high-energy single-layer welding instead of multi-layer welding to study the residual stress distribution trend not only optimizes the weld quality but also reduces the time cost to a large extent.

Research on blind watermarking algorithm of encrypted holographic 3d geographic information data based on mapping mechanism

In order to improve the accuracy and security of encrypted holographic 3D geographic information data acquisition and improve the actual resolution of geographic information files, a blind watermarking algorithm for encrypted holographic 3D geographic information data based on mapping mechanism is proposed. According to the characteristics of the mapping mechanism, a mapping mechanism structure diagram is constructed; Under the mapping mechanism technology, blind watermark data is preprocessed. Then, a watermark embedding operation is performed to obtain the watermark information image, and then a blind watermark that encrypts the holographic three-dimensional geographic information data is extracted. Finally, using the blind watermark signal as input, the blind watermark information is obtained by using the watermark strength, and the holographic 3D geographic data information is segmented and encrypted to complete blind watermark detection. The blind watermark algorithm for encrypting the holographic 3D geographic information data is studied. The results show that the maximum difference between the correlation coefficient of the algorithm in this paper and the correlation coefficient of the unaffected algorithm is only 0.04, which has better anti attack performance, high security, good terrain information collection ability, high data accuracy, and can achieve curvature repair of information data.

Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles

Recently, there has been extensive research and development in the field of smart cars, including technologies related to autonomous driving. Various industries are actively working towards creating efficient and safe self-driving cars. Sensor technologies are emerging to prevent traffic accidents and support safe driving in complex environments where human perception may be limited. One of the representative technologies being researched is the use of Frequency Modulated Continuous Wave (FMCW) radar. Automobile manufacturers are improving driving safety by equipping cars with Blind Spot Detection (BSD) radar systems that use FMCW technology. As the complexity of driving environments continues to grow, ongoing research is aimed at enhancing the accuracy and reliability of BSD radar technology for detecting blind spots in vehicles. This paper presents the signal processing and tracking algorithms that are the core technologies of the BSD radar, and the design for a BSD radar system. The designed radar system was installed on a vehicle to verify its performance in real-world road environments. The ultimate objective of this research is to design a BSD radar system with high accuracy and reliability in BSD detection using AI technology. In pursuit of this goal, this paper presents the hardware design of the BSD radar system, including antenna and modem designs.