Search Results (70)

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

Traditional 2D optical markers degrade quickly in underwater applications due to sediment accumulation and marine biofouling, becoming undetectable within weeks. This paper presents a Three-Dimensional Binary Marker, a novel passive fiducial marker designed for underwater Long-Term Deployment. The Three-Dimensional Binary Marker addresses the 2D-markers limitation through a 3D design that enhances resilience and maintains contrast for computer vision detection over extended periods. The proposed solution has been validated through simulation, water tank testing, and long-term sea trials for 5 months. In each stage, the marker was compared based on detection per visible frame and the detection distance. In conclusion, the design demonstrated superior performance compared to standard 2D markers. The proposed Three-Dimensional Binary Marker provides compatibility with widely used fiducial markers, such as ArUco and AprilTag, allowing quick adaptation for users. In terms of fabrication, the Three-Dimensional Binary Marker uses additive manufacturing, offering a low-cost and scalable solution for underwater localization tasks. The proposed marker improved the deployment time of fiducial markers from a couple of days to sixty days and with a range up to seven meters, providing robustness and reliability. As the marker survivability and detection range depend on its size, it is still a valuable innovation for Autonomous Underwater Vehicles, as well as for inspection, maintenance, and monitoring tasks in marine robotics and offshore infrastructure applications. Full article

In periodic technical inspections (PTIs), evaluating the braking efficiency of light passenger vehicles at their Maximum Authorized Mass (MAM) presents a practical challenge, as bringing laden vehicles to inspection is often unfeasible due to logistical and infrastructure limitations. The fBrake method is proposed to overcome this issue by estimating braking efficiency at MAM based on measurements taken from vehicles in more accessible loading conditions. In this study, the fBrake method is validated by demonstrating the equivalence of its efficiency estimates extrapolated from two distinct configurations: an unladen state near the curb weight and a partially laden condition closer to MAM. Following the UNE 26110 standard (Road vehicles. Criteria for the assessment of the equivalence of braking efficiency test methods in relation to the methods defined in ISO 21069), roller brake tester measurements were used to obtain force data under both conditions. The analysis showed that the extrapolated efficiencies agree within combined uncertainty limits, with normalized errors below 1 in all segments tested. Confidence intervals were reduced by up to 74% after electronics update. These results confirm the reliability of the fBrake method for M1 and N1 vehicles and support its adoption as an equivalent procedure in compliance with UNE 26110, particularly when fully laden testing is impractical. Full article

The degradation and loss of arable soils pose significant challenges to global food security, requiring advanced mapping and monitoring techniques to improve soil and crop management. This study evaluates the integration of Unmanned Aerial Vehicles (UAVs) and orbital sensor data for monitoring and quantifying gullies with low-cost data. The research focuses on a gully in southern Brazil, utilizing high-spatial-resolution imagery to analyze its evolution over a 25-year period (2000–2024). Photointerpretation and manual delineation procedures were adopted to define gully shoulder lines, based on low-cost and multiple-spatial-resolution data from Google Earth Pro (GEP), UAVs and conventional aerial photographs. Planimetric, volumetric, climatic, and pedological parameters were assessed and evaluated over time. Field inspections supported our interpretations. The results show that gully expansion can be effectively mapped and monitored by combining high-spatial-resolution GEP data with aerial imagery. The gully area has increased by more than 50% over the past two decades, based on GEP data, which were corroborated by submeter-resolution UAV data. The findings indicate that the erosive process remains active, progressing toward the base level. These results provide critical insights for land managers, policymakers, and agricultural stakeholders to implement targeted soil recovery strategies and mitigate further land degradation. Full article

The main objective of the research presented in this article was to analyze the composition of exhaust gases from passenger cars undergoing periodic inspections and to determine the influence of vehicle age, mileage and the applicable EURO emission standard on the level of emissions of individual components of exhaust gases and thus on the environment. The research was carried out at the District Vehicle Inspection Station in Nowy Sącz, using methods for analyzing the composition of exhaust gases and smoke opacity. The results obtained make it possible to assess whether exhaust emission diagnostics can form the basis for the implementation of a sustainable road transport policy. The study showed that older vehicles emit higher concentrations of carbon monoxide (CO) and hydrocarbons (HC), and diesel cars manufactured before 2010 are characterized by increased smoke opacity. A reliable analysis of the emissions performance of vehicles on the road enables more effective measures to be taken to reduce emissions and improve air quality through regulation, the introduction of clean traffic zones and raising environmental awareness among drivers. This is especially important in regions with specific geographical conditions, such as the Nowy Sącz district, where the terrain—Nowy Sącz is located in a basin surrounded by mountain ranges—favors the accumulation of pollutants and hinders the natural air circulation, leading to the long-term persistence of smog. Full article

Roadside incidents are a leading cause of driver fatalities in the United States, with a significant number involving collisions with barriers, such as guardrails. Guardrails are essential safety barriers designed to maintain vehicle trajectories and shield against roadside hazards. The functionality of guardrails heavily relies on their structural integrity, and damaged guardrails can pose serious dangers to road users. Traditional inspection methods are labor-intensive, time-consuming, and prone to human error, lacking periodic monitoring crucial for timely maintenance. Although advancements in computer vision have enabled automated infrastructure inspections, research dedicated specifically to the inspection of guardrails remains scarce. Existing automated solutions do not fully address the challenges of accurately identifying and assessing guardrail damage under varying lighting and weather conditions and the computational demands of real-time processing. This study addresses these challenges by introducing a novel framework utilizing advanced computer vision techniques, such as YOLOv8 models and the Deep OC–SORT tracker, integrated with camera and GPS systems mounted on a vehicle. This system automates the detection, localization, and severity assessment of guardrail damage, enhancing inspection accuracy and efficiency, enabling faster maintenance responses, and ultimately contributing to safer road conditions. Full article

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally. Full article

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M₁ and N₁ vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections. Full article

Thousands of particle number (PN) counters have been introduced to the European market, following the implementation of PN tests during the periodic technical inspection (PTI) of diesel vehicles equipped with particulate filters. Expanding the PN-PTI test to gasoline vehicles may face several challenges due to the different exhaust aerosol characteristics. In this study, two PN-PTI instruments, type-examined for diesel vehicles, measured fifteen petrol passenger cars with different test protocols: low and high idling, with or without additional load, and sharp accelerations. The instruments, one based on diffusion charging and the other on condensation particle counting, demonstrated good linearity compared to the reference instrumentation with R-squared values of 0.93 and 0.92, respectively. However, in a considerable number of tests, they registered higher particle concentrations due to the presence of high concentrations below their theoretical 23 nm cut-off size. The evaluation of the different test protocols showed that gasoline direct injection engine vehicles without particulate filters (GPFs) generally emitted an order of magnitude or higher PN compared to those with GPFs. However, high variations in concentration levels were observed for each vehicle. Port-fuel injection vehicles without GPFs mostly emitted PN concentrations near the lower detection limit of the PN-PTI instruments. Full article

Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it is critical to diagnose the condition of the windings. The objective of this article is to establish a method to identify the short-circuit fault points in concentrated stator windings based on the magnetic flux density distribution near the stator windings. Unlike with distributed windings, the coils are wound around the teeth in concentrated windings. Thus, it is expected that the accurate position specification of the short circuit can be realized if a detailed magnetic flux density distribution over the teeth is obtained with an appropriate magnetic field sensor. The problem of sensor positioning is solved with two stepper motors moving the search coil in the rotational and longitudinal directions independently at specified intervals. The excellent capability of the proposed system is verified through experiments using the stator winding employed in hybrid electric vehicles. The accuracy and sensitivity of the proposed identification system for short-circuit fault points may enable its practical application in industries, for example, shipping and periodic inspections as well as the production management of motors with concentrated stator windings. Full article

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. Full article

(1) Background: Ensuring road user safety relies on the optimal technical condition of cars, addressing both active and passive safety measures. In Poland, vehicle regulations, articulated in the Minister of Infrastructure’s decree of 31 December 2002, establish technical prerequisites and necessary equipment. For this purpose, the main question was: What is the current technical condition of cars on the road in Western Poland? (2) Methods: A total of 1067 vehicles were tested, reflecting a maximum error of 3% in a population of 20 million cars. Tests were conducted at the diagnostic station from 1 October 2022 to 30 September 2023. Statistical analysis was conducted using STATISTICA software. (3) Results: Periodic technical tests yield insights into passenger car safety standards in western Poland. The application of formulated characteristics allows a comprehensive evaluation, providing valuable information on the overall safety condition of inspected vehicles. The vehicles in Poland have an average age exceeding 14 years, and their average mileage is 168,000 km. (4) Conclusions: The examination uncovered various technical defects and provided statistical interpretations, unequivocally demonstrating that these identified issues have the potential to impact traffic safety. Such studies act as a reference point for other researchers addressing the broader issue of road traffic. Full article

The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate. Full article

Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process. Full article

A Periodic Technical Inspection (PTI) of vehicles promotes road safety and environmental protection. Indeed, a PTI is also used to verify the proper functioning of the vehicle’s aftertreatment system (ATS) over its lifetime. While the current Directive 2014/45/EU, which covers the PTI, does not require a NOx emissions measurement, the ongoing revision of the roadworthiness package aims at including new methods for measuring exhaust NOx and particle number (PN) emissions. PTI tests are required to be simple, quick, inexpensive and effective. In this study, a new methodology for a NOx measurement during the PTIs of Diesel vehicles equipped with a selective catalytic reduction (SCR) unit is assed. Seven Euro 6 light-duty Diesel vehicles fulfilling post-Real Driving Emissions (RDE) regulations were tested. The NOx-PTI methodology consists of measuring NOx emissions from the vehicle tailpipe at engine low idle speed after properly conditioning the vehicle ATS. In such conditions, a well-functioning SCR unit reduced NOx emissions and the methodology proved to be suitable to discriminate between functioning and malfunctioning SCR systems. Full article