Efficient Evaluation of the Number of False Alarm Criterion

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds, and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion, and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results, and underlines its potential for handling complex pattern detection tasks

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

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