Thu Huong Nguyen - On Road Defects Detection and Classification

IntroductionData Collection

Construction of Map of DefectsDefects Detection and Classification Method on Road Pavement

ConclusionIrkutsk State Technical University

On road defects detection and classification

Thu Huong Nguyen, Aleksei Zhukov, The Long Nguyen

Irkutsk State Technical University

The 7th to 9th April 2016

Nguyen Thu Huong, Aleksei Zhukov, The Long NguyenAIST 2016 On road defects detection and classification 1 / 21




Outline

Introduction

Data Collection

Construction of Map of Defects

Defects Detection and Classification Method on Road Pavement

Conclusion





Introduction

• In this talk proposes an automatic defect pavement detectionand classification system capable of identifying and retrievingpavement surface images containing block cracks, longitudinalcracks, potholes from a road pavement survey image database.

• The experimental results, achieved using images from Irkutstroads, are encouraging for the development of automaticpavement defects detection systems.





Motivation





Objective

• We proposes algorithms: Graph cuts method for imagessegmentation, Random forest for classification and imageprocessing algorithms for features extraction.

• Automatic learning methods are used, capable of learning theimage statistical features from texture variations of the roadbackground and of the defects areas.

• The features studied are Histogram, Histogram chain code,Moments-hull, shape of features.





Main steps

1.To detect defect position (ROI).2.Defect is described by its features.3.To classify defect each using these different defect features suchas Chain Code Histogram, Hu-Moments, size of defectregion(width and length, area) and histogram of image.

Our approach

The following algorithms have been used: Graph cuts method forimage segmentation, Random Forests algorithm for dataclassification.





Data Collection

We used two datasets:

1 Our own dataset include:• 500 images are collected by camera (Canon D100 16 mega

pixel).• Images are captured in conventional daylight condition.• Distance from camera to surface of road is 1m-1.2m.

2 SARA• More 700 images.• Collection by Center for Telecommunications and Multimedia,

INESC TEC, Portugal.





Segmentation by Graph cuts method

• Graph cuts uses power optimization algorithm, which isapplied specifically to those models which employ amax-flow/min-cut optimization (other graph cuttingalgorithms may be considered as graph partitioningalgorithms).

• Finds strong local minima of our np-complete energy function.

• Graph-cuts have been around in computer vision for quitesome time (e.g. [Roy,ICCV98]).





Segmentation by Graph cuts method

Figure: Example Graph cuts segmentation method





Max-Flow Problem

Task : Maximize the flow from the sink to the source such that:

• The flow it conserved for each node

• The flow for each pipe does not exceed the capacity





Ford Fulkerson algorithm (1956)

Input: Given a network G = (V ,E ) with flow capacity c , a sourcenode s, and a sink node t

Output: Compute a flow f from s to t of maximum value.f (u, v)← 0 for all edges (u, v)while there exists a path p from s to t in the residual network Gj

doFind cf (p) = min {cf (u, v) | (u, v) ∈ p} for eachedges(u, v) ∈ p do

f [u, v ] = f [u, v ] + cf (p);f [v , u] = f [v , u]− cf (p);

end

end

Algorithm 1: Ford Fulkerson algorithm (1956)





Result of max flow





Min-Cut Problem

Task : Minimize the cost of the cut

• Each node is either assigned to the source S or sink T

• The cost of the edge (i , j) is taken if (i ∈ S) and (j ∈ T )

Finding min-cut |C | =∑

e∈C we





Result of Graph cut segmentation

Figure: Fig(a) Result of Graph cut segmentation method. Fig(b) Resultof Random forest algorithm





Flowchart

End

Load model classification of

machine learning

Load road pavement image

database

Classification based on

RandomForest algorithm

Return type of defect road

pavement

Create features vector

Features extraction

Preprocessing image

Begin





Random Forest algorithm

• Random forest (or random forests) is an ensemble classifierthat consists of many decision trees and outputs the class thatis the mode of the class’s output by individual trees.

• The method combines Breiman’s ”bagging” idea and therandom selection of features.





Flowchart

Figure: Flow chart of Random Forest algorithm [Girish (2015)].





Random Forest practical consideration

• Splits are chosen according to a purity measure:E.g. squared error (regression), Gini index or devinace(classification)

• How to select N?Build trees until the error no longer decreases

• How to select M?Try to recommend defaults, half of them and twice of themand pick the best.





Training time, Correct rate and Error test of RandomForest classification algorithm

Random Forest 100 trees 50 trees 100 trees 100 treesdepth:2 depth:2 depth:5 depth:10

Training time(sec) 250 150 50 140

Correct rate (%) 91.45 80.5 93.29 96.66

MSE 0.393 0.516 0.366 0.3

Table: Training time, Correct rate and Error test of Random Forestclassification algorithm





Conclusion

• In this talk we suggested the novel approach for roadpavements defects automatic detection and classification.This method is based on the construction of an irregularlattice derived from the original image. The lattice iscomposed only by straight line segments.

• We also propose to use to Graph cut method, which improvequality of image segmentation. From this we can detectionpart of pavement defect - non defect.

• The classification algorithm - Random Forest was able tocorrectly classify all the images contained in the two first sets.In the test set simulating the real environment the achievedclassification results were 95,5%.





THANK YOU SO MUCH !!!


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Thu Huong Nguyen - On Road Defects Detection and Classification