Vehicle Inspection Research Articles

A visual area detection algorithm based on mirror splicing for motorcycle indirect vision test

Abstract Motorcycle indirect vision devices are important safety components, and the visual area is required to meet industry standards. There is a need for effective algorithms to detect and analyze the relevant visual areas within these mirrors to enhance motorcycle safety. However, it is a challenge to test rearview mirror vision in unstructured environments given variations in illumination, occlusion, and object scale. We propose a visual area detection algorithm based on mirror stitching for a motorcycle indirect vision test. First, an edge-based VGG16-Unet (EBV16-Unet) network is employed to extract binocular mirror information and eliminate the complex background. Second, gradient-based topology-preserving image stitching and multi-band hybrid Laplacian pyramid-based image blending algorithms are utilized to complete binocular mirror information acquisition. Finally, a sequential detection method for adaptive marker color and shape features is used to establish the visual area. The EBV16-Unet algorithm achieved an accuracy of 98.63% for precision, 98.71% for recall, 98.58% for F1, and 98.37% for mean intersection-over-union (MIOU), surpassing the comparative models of PSPNet, DeepLab v3+, and HRNet and exhibited superior generalization ability. The binocular vision splicing effect experiment revealed a horizontal splicing error of 0.114322 ± 0.0674 and vertical splicing error of 0.124287 ± 0.063302, calculated using a standard checkerboard. The rearview mirror vision test operation experiment results confirm that the Motorcycle Indirect Vision Test System (MIVTS) offers convenience, simplicity and high accuracy. MIVTS successfully accomplishes the unstructured motorcycle rearview mirror vision test, thereby establishing an advanced theoretical foundation for computer vision-based automated vehicle inspection.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

Train wheel-rail force collaborative calibration based on GNN-LSTM

Accurate wheel-rail force data serves as the cornerstone for analyzing the wheel-rail relationship. However, achieving continuous and precise measurement of this force remains a significant challenge in the field. This article introduces a calibration algorithm for the wheel-rail force that leverages graph neural networks and long short-term memory networks. Initially, a comprehensive wheel-rail force detection system for trains was constructed, encompassing two key components: an instrumented wheelset and a ground wheel-rail force measuring system. Subsequently, utilizing this system, two distinct datasets were acquired from the track inspection vehicle: instrumented wheelset data and ground wheel-rail force data, a feedforward neural network was employed to calibrate the instrumented wheelset data, referencing the ground wheel-rail force data. Furthermore, ground wheel-rail force data for the locomotive was obtained for the corresponding road section. This data was then integrated with the calibrated instrumented wheelset data from the track inspection vehicle. Leveraging the GNN-LSTM network, the article establishes a mapping relationship model between the wheel-rail force of the track inspection vehicle and the locomotive wheel-rail force. This model facilitates continuous measurement of locomotive wheel-rail forces across three typical scenarios: straight sections, long and steep downhill sections, and small curve radius sections.

Reinforcement learning based robot navigation using illegal actions for autonomous docking of surface vehicles in unknown environments

Autonomous Surface Vehicles (ASVs) are bound to play a fundamental role in the maintenance of offshore wind farms. Robust navigation for inspection vehicles should take into account the operation of docking within a harbouring structure, which is a critical and still unexplored maneuver. This work proposes an end-to-end docking approach for ASVs, based on Reinforcement Learning (RL), which teaches an agent to tackle collision-free navigation towards a target pose that allows the berthing of the vessel. The developed research presents a methodology that introduces the concept of illegal actions to facilitate the vessel’s exploration during the learning process. This method improves the adopted Actor-Critic (AC) framework by accelerating the agent’s optimization by approximately 38.02%. A set of comprehensive experiments demonstrate the accuracy and robustness of the presented method in scenarios with simulated environmental constraints (Beaufort Scale and Douglas Sea Scale), and a diversity of docking structures. Validation with two different real ASVs in both controlled and real environments demonstrates the ability of this method to enable safe docking maneuvers without prior knowledge of the scenario.

Perception of drivers toward road safety and factors that cause road accidents in Riyadh city of Saudi Arabia

Injury as a result of road traffic accidents (RTA) is one of the leading causes of death and disability-adjusted life years. Strategies to handle this major public health problem, which has significant consequences for the Saudi economy and families, have been underway for many years but with very little success. This study’s main aim is to determine respondents’ perceptions of traffic safety and the factors that lead to road traffic accidents. It is a cross-sectional study conducted on randomly selected university students, faculty drivers, and visitors using King Saud University’s parking facility. Data were collected from 381 drivers using a pretested questionnaire, which comprises questions intended to attain the study’s objectives. The critical factors affecting traffic safety are public information followed by road maintenance, Garage, vehicle inspection, Road design and equipment, Training of drivers, Sanctions and Police enforcement, Protective devices and vehicle design, and post-accidental care. Road traffic accidents were found to be 39.0% with respondents aged less than 20 years, followed by ages between 20–35 years, which was 34.8%, and only 2.4% above 50 years. The most frequent occurrence of road traffic accidents was angle collision (39.0%) and back collision (18.9%), followed by hitting a fixed object (17.7%). Distraction habits during driving include cell phone usage (56.7%), followed by smoking (24.4%), and eating and drinking (18.9%) while driving. The most common traffic violations were increased speed limit (48.1) and loss of driving license (11.8%). The most common injuries were head (39.0%) and limbs (22.0%). A significant association between drivers’ age during the accident and distraction habits has been found (p < 0.000). The study demonstrated that drinking/eating, cell phone use, and violation of traffic rules are significant reasons for RTA. The findings of this study will contribute to a more comprehensive knowledge of road traffic accidents for the formation of effective strategies and evidence-based policies for the prevention of accidents.

ОЦЕНКА УСТОЙЧИВОСТИ ОБШИВКИ БОКОВЫХ СТЕН И КРЫШИ КУЗОВА ПАССАЖИРСКОГО ВАГОНА ПРИ ВЫПОЛНЕНИИ РЕМОНТНЫХ РАБОТ

The lining stability of the side walls and roof of the passenger car body is assessed during repair work related to its asymmetric lifting with a jack at one end of the body. Using mathematical modeling, the lifting height is determined which is necessary to ensure the possibility to dismantle the springs of the spring set of the central suspension. The stability of the reinforced lining of the car body was assessed using the developed problem-oriented elastic-dissipative finite element model of the supporting structure of the passenger car body. The calculations are performed in a static non-linear formulation, taking into account the geometric nonlinearity and non-conservativeness of the operating loads. The stability of the reinforced load-bearing lining of the car was assessed both for the initial structure and for a structure with geometric imperfections such as technological loss. The analysis of the research results allows to conclude that during the technological operation associated with lifting the passenger car body with a jack under one support, it can lead to a loss of stability of the sill belt lining of the side wall and the smooth sheathing of the roof slope. The results obtained agree with the results of the inspection of full-scale cars in operation.

DEVELOPMENT OF A DATABASE CONCEPT FOR DIGITALISING THE EXPERIENCE OF BUILDINGS AND STRUCTURES OPERATING

Problem statement. These days, the main method of monitoring the buildings and structures state is through periodic technical state assessment and monitoring. This is an important practice that guarantees the reliability and safety of the operation of buildings and structures, helping to identify and establish the obvious and possible causes of defects and damages. However, the current practice of storing the results of surveys and monitoring is often limited to records in paper reports, which makes it difficult to analyse over time. Thus, there is a need to create a digital information system designed to record and store the results of monitoring of buildings and structures. This will make the data accessible and organised for specialists, simplifying analysis and ensuring their effective use for decision-making. Such a system will significantly improve the safety and efficiency of buildings and structures management. The purpose of the article is the development of a database architecture for the digitalisation of information related to the inspection and monitoring of the technical state of buildings and structures. Conclusions. The developed database structure will allow storing information about buildings, their owners, location, structures, defects and damage identified during visual and instrumental inspections, as well as organisational and technical measures aimed at bringing structures back to normal technical state or reducing the impact of negative factors. This will contribute to more efficient management and help maintain the property in good working order, reducing the probability of emergency situations.

Тактические особенности осмотра транспортных средств, имеющих бортовые компьютеры, при расследовании дорожно-транспортных преступлений

The article analyzes the inspection of vehicles with on-board computers for the possibility of obtaining information relevant to the investigation of a traffic crime. When examining a vehicle, the investigator must take measures to obtain information about the presence of an on-board computer and subsequently to detect it, for this purpose, seek help from an automotive technician, a specialist in the field of computer technology, or car dealers. The information received from the on-board computer is used in the course of conducting a forensic auto-technical examination.

Interactive technology of high voltage tower acceptance UAV Based on AR and YOLOv7

To address many shortcomings of the current unmanned aerial vehicle (UAV) inspection mode of the transmission line high-voltage towers and improve the inspection efficiency, the UAV tower foreign matter detection technology based on Augmented Reality (AR) and You Only Look Once: Unified, Real-Time Object Detection (YOLOv7) has been developed. Firstly, the system architecture, including UAV, ground station, and AR glasses is established. Then, the AR scene is built by using the Unity engine, and the interactive control mode between AR glasses and UAV is designed. Subsequently, the mapping mode of head action and UAV command is devised to synchronize the UAV action and head action. Finally, the tower is detected for foreign objects by using YOLOv7 vision detection technology. The experimental results validate the efficacy of the built AR scene, demonstrating seamless functionality in completing the video streaming and attitude data transmission functions. The use of AR glasses can achieve interactive control of the UAV attitude. In addition, the YOLOv7 visual detection algorithm can accurately detect the attachment of foreign objects to the high-voltage tower.

Research on adaptive shooting algorithm for unmanned aerial vehicles under typical operating conditions of overhead transmission lines

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects.

Optimizing 2D path planning for unmanned aerial vehicle inspection of electric transmission lines

The inspection of electric power transmission lines using unmanned aerial vehicles represents an innovative maintenance approach that several countries, including Morocco, are considering adopting. The primary aim is to enhance existing procedures, capitalize on their advantages, and address challenges associated with traditional methods. Given the precision, efficiency, and safety required for this application to ensure reliable results, we propose a functional architecture for unmanned aerial vehicle-based electric power transmission line inspection. This architecture encompasses key phases such as data collection, planning, and inspection, while identifying electromagnetic interferences and minimum approach distances as major constraints. To streamline mission planning, we introduce a trajectory optimization algorithm integrated into a mission planning assistance application for transmission line inspection. This algorithm presents a novel approach by hybridizing three algorithms: genetic algorithm, ant colony optimization and particle swarm optimization. Consequently, our work represents a significant contribution to the integration of unmanned aerial vehicles in electric power transmission lines inspection operations for electrical energy transport, facilitating potential advancements and discoveries in the field.

Automatic Detection of Ballast Unevenness Using Deep Neural Network

The amount of freight transported by rail and the number of passengers are increasing year by year. Any disruption to the passenger or freight transport stream can generate both financial and human losses. Such a disruption can be caused by the rail infrastructure being in poor condition. For this reason, the state of the infrastructure should be monitored periodically. One of the important elements of railroad infrastructure is the ballast. Its condition has a significant impact on the safety of rail traffic. The unevenness of the ballast surface is one of the indicators of its condition. For this reason, a regulation was introduced by Polish railway lines specifying the maximum threshold of ballast unevenness. This article presents an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by the Polish railway lines. The proposed solution has the following elements of novelty: (a) it presents a simple criterion for evaluating the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper; (b) it treats ballast irregularity detection as an instance segmentation process and it compares two segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection; and (c) it uses segmentation-related metrics—mAP (Mean Average Precision), IoU (Intersection over Union) and Pixel Accuracy—to evaluate the quality of the detection of ballast irregularity.

Elevating vehicle inspection quality through knowledge: unlocking excellence

This study analyses work performance factors, the ability to master work and work tools, and knowledge of the quality of work results of competent civil servants (PNS) in regularly inspecting motor vehicles in Indonesia. This study aims to determine knowledge as a mediating variable in the influence of work performance and the ability to master work and work tools on the quality of work results and its improvement strategy. The study's sample size was 180 people, and the total population was statistical test research using descriptive statistics with structural equation models using PLS-SEM. The study's results found that work performance and the ability to master work and tools did not directly affect the quality of work results. However, the mediation of variables of work performance knowledge and the ability to master work and work tools had a positive and significant effect on the quality of work results. Knowledge directly affects the quality of the results of work. The variables of work performance and the ability to master work and work tools directly affect understanding. Researchers suggest future research look for variable indicators of work performance, the ability to master work and tools, and learning to affect the quality of work results directly or indirectly.

Improving the Application of Yolov7 Algorithm in Pin-Losing-Bolts Detection on Power Transmission Lines

A method for bolt deficiency detection on power transmission lines based on YOLOv7 is proposed to address the issues of low detection accuracy and high false negatives in current unmanned aerial vehicle (UAV) inspections. In the Neck module, the existing feature fusion structure is modified by directly combining deep and shallow feature information across adjacent scale feature maps. The prior box sizes in the original YOLOv7 are adjusted to better fit the dimensions and aspect ratios of bolt targets. The improved YOLOv7 algorithm enhances the learning capability of bolt feature information. Experimental results on the bolt deficiency dataset using this method show a 6% increase in precision and a 0.6% increase in mAP_0.5. The experiments demonstrate that the improved method enhances the detection ability of bolt deficiencies on power transmission lines and has practical value in intelligent inspections.

Based on an Overview of Crack Detection

This Crack detection, as an important part of concrete structural health monitoring, aims to reflect the stress state and damage degree of the structure. Traditional concrete crack detection mainly relies on manual visual identification, which has low detection efficiency and accuracy. In addition, manual visual detection has problems such as being greatly affected by lighting conditions, being unable to cover overhead locations such as bridge towers and high piers, and being highly subjective. In recent years, in order to solve the above problems, researchers at home and abroad have developed concrete crack detection equipment based on digital image technology, such as inspection vehicles equipped with high-resolution cameras, unmanned aerial vehicles, crawling robots, and so on. Meanwhile, efficient and accurate crack detection algorithms are the basis for realizing accurate crack identification. How to balance the detection speed and accuracy has been the focus of academic attention. This paper reviews the research progress of concrete crack detection equipment based on digital image technology at home and abroad in recent years, including camera platform, calibration method, preprocessing, traditional and deep learning algorithms, crack feature extraction, image stitching, and 3D representation and monitoring of cracks. In addition, the deficiencies in the research are summarized.

Estimating Hazard Function through Reliability Function and Empirical Methods

In this research, the reliability functions are applied to estimate the hazard function of four used car components (tires, brakes, lights, engine), which are inspected by aperiodic vehicles inspection (PVI) established in Erbil city, as a specialized company that conducts the annual technical inspection of vehicles to detect the failure component, that either require repair or replace it with a new one. For our purpose, the data about the failure components of a sample of size (50000) cars are obtained from the Erbil traffic directorate, which are annually inspected for 11 years (2010 – 2020) by a (PVI) company. From the available data the reliability function, hazard function, and probability density function of failure time of each component are found by the non-parametric method and the estimated Rayleigh distribution since the failure rates of the components are the linear functions of time, also the comparison between their reliability values have made by the mean absolute error method.

Online insulator defects detection and application based on YOLOv7-tiny algorithm

As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7-tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects.

Applying Machine Learning to Construct a Printed Circuit Board Gold Finger Defect Detection System

Machine vision systems use industrial cameras’ digital sensors to collect images and use computers for image pre-processing, analysis, and the measurements of various features to make decisions. With increasing capacity and quality demands in the electronic industry, incoming quality control (IQC) standards are becoming more and more stringent. The industry’s incoming quality control is mainly based on manual sampling. Although it saves time and costs, the miss rate is still high. This study aimed to establish an automatic defect detection system that could quickly identify defects in the gold finger on printed circuit boards (PCBs) according to the manufacturer’s standard. In the general training iteration process of deep learning, parameters required for image processing and deductive reasoning operations are automatically updated. In this study, we discussed and compared the object detection networks of the YOLOv3 (You Only Look Once, Version 3) and Faster Region-Based Convolutional Neural Network (Faster R-CNN) algorithms. The results showed that the defect classification detection model, established based on the YOLOv3 network architecture, could identify defects with an accuracy of 95%. Therefore, the IQC sampling inspection was changed to a full inspection, and the surface mount technology (SMT) full inspection station was canceled to reduce the need for inspection personnel.

Особенности проведения гласного оперативно-разыскного мероприятия «Обследование помещений, зданий, сооружений, участков местности и транспортных средств» при выявлении незаконных лабораторий по производству синтетических наркотических средств и психотропных веществ

Purpose: to consider combating the illegal production of synthetic narcotic drugs and psychotropic substances in clandestine laboratories with a public investigative activity ‘Inspection of premises, buildings, structures, areas and vehicles’. Research methods: the methodological basis of the work is represented by such methods as description, analysis, and reference to statistical data. Conclusion based on the results of the study: based on the analysis of law enforcement practice, the importance of the public operational-search activity ‘Inspection of premises, buildings, structures, areas and vehicles’ was determined. Scientific and practical significance: highlighting the distinctive features of the public operational-search activity ‘Inspection of premises, buildings, structures, areas of terrain and vehicles’ in identifying and solving crimes related to the production of narcotic drugs and psychotropic substances.

The New Cyberlinktech, Inc. Resilience to Creating Growth Model

The New Cyberlinktech, Inc. (TNCI) operates in a highly dynamic and evolving market, requiring the need for a strategic business plan to sustain growth and adaptability. The New Cyberlinktech, Inc. (TNCI) is one of the leading information technology providers for Private Emission Testing Centers (PETCs) under the Department of Transportation and the Land Transportation Office in the Philippines. TNCI faces significant challenges in the current market landscape, including increasing regulatory pressure against PETCs, the emergence of the Motor Vehicle Inspection System (MVIS) as an alternative mode of vehicle testing, and the frequent leadership changes and abrupt policy shifts at the Land Transportation Office (LTO) and the Department of Transportation (DOTr). To ensure sustainability and adaptability, this dissertation aims to develop a comprehensive business plan for The New Cyberlinktech, Inc. (TNCI) that addresses the challenges posed by the rapidly changing market landscape including a 5-year roadmap. The research design employs a mixed methods approach. The qualitative phase involves conducting interviews with key stakeholders within TNCI, including senior management, employees, and selected industry experts. Through these qualitative interactions, the study aims to identify the specific challenges faced by TNCI in the evolving market and explore potential strategic approaches to address them. In the quantitative phase, the focus is on using surveys among the current clients of the company to determine the preferred IT projects that they want the company to implement in the future. The quantitative analysis provides a comprehensive assessment of TNCI’s current performance, identifies potential risks and opportunities, and informs the development of strategic recommendations. The integrated analysis phase synthesizes the findings from the qualitative and quantitative data. By comparing and contrasting the insights gained from both approaches, a holistic understanding of the challenges and opportunities facing TNCI is achieved. The strategic business plan is formulated based on the integrated analysis, considering the specific needs and goals of TNCI. The plan encompasses strategies to address regulatory challenges, navigate the MVIS landscape, manage leadership changes, adapt to policy shifts, and recommend potential IT projects that the company can adapt and implement. Potential findings may include insights into the specific regulatory pressures faced by PETCs, the implications of the MVIS on TNCI’s business model, and strategies to mitigate the effects of leadership and policy changes. The implications of the research findings can inform TNCI’s decision-making process and help guide the company in sustaining growth and adaptability in the rapidly evolving market. This dissertation contributes to the body of knowledge by providing a comprehensive strategic business plan tailored to TNCI’s context. The findings and recommendations have practical implications for TNCI and other IT companies operating in similar environments. Future research opportunities may involve evaluating the implementation and effectiveness of the proposed business plan and exploring additional strategies to enhance TNCI’s competitive advantage in the PETC industry. Keywords: Strategic continuity, Business plan, Sustainability, Adaptability, Private Emission Testing Centers, Motor Vehicle Inspection System, Regulatory pressure, Leadership change, Policy shifts, mixed methods researc