With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Road networks are the backbone of urban life and significantly impact the sustainability of any country's infrastructure sector.Therefore,it is necessary to maintain the condition of roads and pavements through co...Road networks are the backbone of urban life and significantly impact the sustainability of any country's infrastructure sector.Therefore,it is necessary to maintain the condition of roads and pavements through continuous monitoring and periodic maintenance in order to achieve the highest levels of service for road users and the sustainability of their use.Pavement is the main component of road networks,providing the highest degree of comfort to drivers and roadway users when it is appropriately designed and free from defects and cracks.More clearly,defects are one of the most important factors that reduce the operational life of roads and cause economic losses to road users by causing damage to their vehicles;moreover,the damaged pavement needs frequent and long maintenance that may also drain the resources of government institutions and transport agencies.Therefore,there is a crucial need for a monitoring and follow-up system for the condition of the roads in order to identify and treat defects quickly.This study used a vibration-based system to monitor pavement conditions on several roads with different gradients.A fully electric car was used to determine the vibration values,which indicate the degree of driving comfort,to determine the spread and behaviour of defects on the pavement at multiple locations on roads with different gradients.Also,a machine learning model was applied using a“decision tree”model to identify,classify and predict defects on the pavements.The results of this study indicated that pavement defects were more prevalent in the first and last quadrants of the high-slope roads compared to the low-slope roads.The prediction model achieved accuracy in predicting the performance of defects with a rate of 94%for roads with low gradients and 90%and 86%for roads with medium and high gradients,respectively.展开更多
Intersection-related crashes are associated with high proportion of accidents involving drivers, occupants, pedestrians, and cyclists. In general, the purpose of intersection safety analysis is to determine the impact...Intersection-related crashes are associated with high proportion of accidents involving drivers, occupants, pedestrians, and cyclists. In general, the purpose of intersection safety analysis is to determine the impact of safety-related variables on pedestrians, cyclists and vehicles, so as to facilitate the design of effective and efficient countermeasure strategies to improve safety at intersections. This study investigates the effects of traffic, environ- mental, intersection geometric and pavement-related characteristics on total crash fre- quencies at intersections. A random-parameter Poisson model was used with crash data from 357 signalized intersections in Chicago from 2004 to 2010. The results indicate that out of the identified factors, evening peak period traffic volume, pavement condition, and unlighted intersections have the greatest effects on crash frequencies. Overall, the results seek to suggest that, in order to improve effective highway-related safety countermeasures at intersections, significant attention must be focused on ensuring that pavements are adequately maintained and intersections should be well lighted. It needs to be mentioned that, projects could be implemented at and around the study intersections during the study period （7 years）, which could affect the crash frequency over the time. This is an important variable which could be a part of the future studies to investigate the impacts of safety related works at intersections and their marginal effects on crash frequency at signalized intersections.展开更多
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
文摘Road networks are the backbone of urban life and significantly impact the sustainability of any country's infrastructure sector.Therefore,it is necessary to maintain the condition of roads and pavements through continuous monitoring and periodic maintenance in order to achieve the highest levels of service for road users and the sustainability of their use.Pavement is the main component of road networks,providing the highest degree of comfort to drivers and roadway users when it is appropriately designed and free from defects and cracks.More clearly,defects are one of the most important factors that reduce the operational life of roads and cause economic losses to road users by causing damage to their vehicles;moreover,the damaged pavement needs frequent and long maintenance that may also drain the resources of government institutions and transport agencies.Therefore,there is a crucial need for a monitoring and follow-up system for the condition of the roads in order to identify and treat defects quickly.This study used a vibration-based system to monitor pavement conditions on several roads with different gradients.A fully electric car was used to determine the vibration values,which indicate the degree of driving comfort,to determine the spread and behaviour of defects on the pavement at multiple locations on roads with different gradients.Also,a machine learning model was applied using a“decision tree”model to identify,classify and predict defects on the pavements.The results of this study indicated that pavement defects were more prevalent in the first and last quadrants of the high-slope roads compared to the low-slope roads.The prediction model achieved accuracy in predicting the performance of defects with a rate of 94%for roads with low gradients and 90%and 86%for roads with medium and high gradients,respectively.
文摘Intersection-related crashes are associated with high proportion of accidents involving drivers, occupants, pedestrians, and cyclists. In general, the purpose of intersection safety analysis is to determine the impact of safety-related variables on pedestrians, cyclists and vehicles, so as to facilitate the design of effective and efficient countermeasure strategies to improve safety at intersections. This study investigates the effects of traffic, environ- mental, intersection geometric and pavement-related characteristics on total crash fre- quencies at intersections. A random-parameter Poisson model was used with crash data from 357 signalized intersections in Chicago from 2004 to 2010. The results indicate that out of the identified factors, evening peak period traffic volume, pavement condition, and unlighted intersections have the greatest effects on crash frequencies. Overall, the results seek to suggest that, in order to improve effective highway-related safety countermeasures at intersections, significant attention must be focused on ensuring that pavements are adequately maintained and intersections should be well lighted. It needs to be mentioned that, projects could be implemented at and around the study intersections during the study period （7 years）, which could affect the crash frequency over the time. This is an important variable which could be a part of the future studies to investigate the impacts of safety related works at intersections and their marginal effects on crash frequency at signalized intersections.
