Parking Camera Calibration for Assisting Automated Road Defect Detection

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Osaka University.Accurate and timely information is essential for efficient road maintenance planning. Current practice mainly depends on manual visual surveys that are laborious, time consuming, subjective and not frequent enough. We overcame this limitation in our previous work, by proposing a method that automatically detects road defects in video frames collected by a parking camera. The use of such a camera leads to capturing the surroundings of the road, such as sidewalks and sky due to its wide field of view. This unnecessarily reduces the method’s performance. This paper presents a process that identifies the correct Region of Interest (myROI). myROI corresponds to the region of the camera’s field of view that corresponds to the road lane, while considering defect inspection guidelines. We use the theory of inverse perspective mapping (IPM) to map the road frame coordinates to world coordinates. The camera specifications, and position, lane width and road defect detection guidelines constitute the parking camera calibration parameters for the calculation of myROI’s span and boundaries. We performed computational experiments in MATLAB to calculate myROI, and validated the results with field experiments, where we used a metric tape to measure the road defects. Preliminary results show that the proposed process is capable of calculating myROI.This material is based in part upon work supported by the National Science Foundation under Grant Number 1031329

Improving road asset condition monitoring

Road networks often carry more than 80% of a country’s total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consist of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, we present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none provide a holistic solution to the problem of road asset condition monitorin

Patch detection for pavement assessment

Pavement management systems rely on comprehensive up-to-date road condition data to provide effective decision support for short, medium and long term maintenance scheduling. However, the cost per mile of the existing condition data collection methods allows only for periodical surveys. This leads to long gaps between inspections and a focus on major roads over rural ones. Therefore, pavement condition monitoring systems that provide inexpensive frequent updates on the road condition are necessary. Such systems would require robust and automatic defect detection methods using low-cost sensors. In this paper, one such method is proposed for detecting road patches from video data acquired by the car's parking camera. A patch is initially detected based on its visual characteristics, which are: 1) it consists of a closed contour and 2) its texture is the same with the surrounding intact pavement. The patch is then passed to a kernel tracker in order to trace it in subsequent video frames. This way redetection is avoided and each patch is reported only once. The method was implemented in a C# prototype and tested with video data consisting of approximately 4000 frames collected from roads in Cambridge, UK. The results show that the suggested method has 84% precision and 96% recall.This material is based upon the work supported by the National Science Foundation (NSF Grant #1031329). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.This is the author accepted version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.autcon.2015.03.01

Automated Detection of Multiple Pavement Defects

Knowing the pavement condition is essential for efficiently deciding on maintenance programs. Current practice is predominantly manual with only 0.4% of inspections happening automatically. All methods in the literature aiming at automating condition assessment focus on two defects at most, or are too expensive for practical application. In this paper, the authors propose a low-cost method that automatically detects pavement defects simultaneously using parking camera video data. The types of defects addressed in this paper are two types of cracks (longitudinal and transverse), patches, and potholes. The method uses the semantic texton forests (STFs) algorithm as a supervised classifier on a calibrated region of interest (myROI), which is the area of the video frame depicting only the usable part of the pavement lane. It is validated using data collected from the local streets of Cambridge, U.K. Based on the results of multiple experiments, the overall accuracy of the method is above 82%, with a precision of more than 91% for longitudinal cracks, more than 81% for transverse cracks, more than 88% for patches, and more than 76% for potholes. The duration for training and classifying spans from 25 to 150 min, depending on the number of video frames used for each experiment. The contribution of this paper is dual: (1) an automated method for detecting several pavement defects at the same time, and (2) a method for calculating the region of interest within a video frame considering pavement manual guidelines.This material is based in part upon work supported by the National Science Foundation under Grant Number 1031329.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via https://doi.org/10.1061/(ASCE)CP.1943-5487.000062