Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (this https URL).

Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone.

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

This paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver’s behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver’s compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6–11% of safety improvements and 5–16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.

Variable speed limit: A microscopic analysis in a connected vehicle environment

In recent years, extensive research has been conducted on pavement distress detection. A large part of these studies applied automated methods to capture different distresses. In this paper, a literature review on the distresses and related detection methods are presented. This review also includes commercial solutions. Thereafter, a gap analysis is conducted which is concluded that in particular the distresses related to pavement micro-texture need serious additional research in order to be implemented in a cost-effective fashion. Depth-related distresses are detectible fairly well, but rely on expensive tools.

/pdf/a-review-on-automated-pavement-distress-detection-methods-304gj2fw0f.pdf

A review on automated pavement distress detection methods

An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement

Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly. Sophisticated road-maintenance strategies can be developed using a pothole database, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts. Recent automatic detection systems, such as those based on vibrations or laser scanning, are insufficient to detect potholes correctly and inexpensively owing to the unstable detection of vibration-based methods and high costs of laser scanning-based methods. Thus, in this paper, we introduce a new pothole-detection system using a commercial black-box camera. The proposed system detects potholes over a wide area and at low cost. We have developed a novel pothole-detection algorithm specifically designed to work with the embedded computing environments of black-box cameras. Experimental results are presented with our proposed system, showing that potholes can be detected accurately in real-time.

https://www.mdpi.com/1424-8220/15/11/29316/pdf

Pothole Detection System Using a Black-box Camera

Existing traffic information acquisition systems suffer from high cost and low scalability. To address these problems, the application of wireless sensor networks (WSNs) has been studied, as WSN-based systems are highly scalable and have a low cost of installing and replacing the systems. Magnetic, acoustic and accelerometer sensors have been considered for WSN-based traffic surveillance, but the use of ultrasonic sensors has not been studied. The limitations of WSN-based systems make it necessary to employ power saving methods and vehicle detection algorithms with low computational complexity. In this paper, we model and analyze optimal power saving methodologies for an ultrasonic sensor and present a computationally-efficient vehicle detection algorithm using ultrasonic data. The proposed methodologies are implemented and evaluated with a tiny microprocessor on real roads. The evaluation results show that the low computational complexity of our algorithm does not compromise the accuracy of vehicle detection.

https://www.mdpi.com/1424-8220/14/8/14050/pdf

Analysis of Vehicle Detection with WSN-Based Ultrasonic Sensors

Existing systems for traffic information acquisition have high costs and low scalability owing to their characteristics such as large size, wired power supplies, and wired communication. To achieve low costs and high scalability, the use of traffic information acquisition systems based on wireless sensor networks (WSNs) has been suggested. However, WSN-based systems have important issues, such as low computing power, limited battery capacity, and high transmission delay. Existing studies on WSN-based acquisition systems have not considered all three of these problems together. Moreover, most studies have focused on theoretical problems rather than practical ones. Therefore, we propose a new system that considers all three limitations of WSN-based systems. In our experiments, we installed our system on real roads for an accurate evaluation. The results show that our system has a high detection accuracy, low power consumption, and low transmission delay.

Traffic Information Acquisition System with Ultrasonic Sensors in Wireless Sensor Networks

Recently, the convergence of information technology with biotechnology, nano-technology, or other technologies has been creating a new paradigm. In the field of transportation, intelligent transport systems (ITSs) — a convergence of information technologies and transportation systems — have been studied. The VSL is one ITS technologies that aims to improve the safety and efficiency of transportation while controlling the speed limit according to traffic circumstances. Existing studies for VSL algorithms have considered only one station to control the traffic. However, it is not appropriate for an urban freeway to be installed with many stations. In this paper, a new VSL algorithm is proposed to enhance the effectiveness of VSL for multiple stations. It is based on the cooperation of stations and the real-time road information. The proposed algorithm consists of 4 steps: first is a “searching bottleneck station,” second is a “calculating a size of congestion,” third is a “calculating the number of controlled stations,” fourth is a “calculating VSL.” In our experiments, the microscopic traffic simulator VISSIM performed our modeling works. The results show that the proposed algorithm improves safety on roads with minimum additional travel time.

Variable speed limit to improve safety near traffic congestion on urban freeways

Recently, the convergence of information technology with biotechnology, nano-technology, or other technologies has been creating a new paradigm. In the field of transportation, intelligent transport systems (ITSs)-a convergence of information technologies and transportation systems-have been studied. The VSL is one ITS technologies that aims to improve the safety and efficiency of transportation while controlling the speed limit according to traffic circumstances. Existing studies for VSL algorithms have considered only one station to control the traffic. However, it is not appropriate for an urban freeway to be installed with many stations. In this paper, a new VSL algorithm is proposed to enhance the effectiveness of VSL for multiple stations. It is based on the cooperation of stations and the real-time road information. The proposed algorithm consists of 4 steps: first is a ”searching bottleneck station,” second is a ”calculating a size of congestion,” third is a ”calculating the number of controlled stations,” fourth is a ”calculating VSL.” In our experiments, the microscopic traffic simulator VISSIM performed our modeling works. The results show that the proposed algorithm improves safety on roads with minimum additional travel time.

Youngtae Jo

Papers

Pothole Detection System Using a Black-box Camera

Analysis of Vehicle Detection with WSN-Based Ultrasonic Sensors

Traffic Information Acquisition System with Ultrasonic Sensors in Wireless Sensor Networks

Variable speed limit to improve safety near traffic congestion on urban freeways

Variable Speed Limit to Improve Safety near Traffic Congestion on Urban Freeways