Enhanced Time-Varying Filter Bank Based Automated

PavementCrack Detection System

By

Clyde A. Lettsome, Ph.D., P.E., M.E.M.

Introduction

Review Dr. Zhou’s Filter Bank Based Distress System

Filter Bank Based Pavement Distress Segmentation Systems

ResultsConclusion & Future Research

Review Dr. Zhou’s Filter Bank Based Distress System

Zhou’s Pavement Distress System

Review Dr. Zhou’s & Others Approach To Filter Bank Based Distress System

Surface textual data is maintained Setting a subjective threshold to define distress

can be difficult [Zhou and Li] Uses highpass subband information to quantify

the amount of distress Zhou’s system does not allow the system to utilize

standard compression coders

Filter Bank Based Pavement Distress Segmentation System

Filter Bank Based Pavement Distress Segmentation System

Understand what defines success is for pavement distress systems

Design with the understanding that the data may have been compressed

Create a system that emulates the humans visual system The ground truth for images are determined by humans and

thus we must use a human approach to identifying cracks Eliminate random data Look for clustering of crack pixels Use the linearity properties of cracks and weber ratio to assist

with detection and segmentation (isolation) Build a filter bank based distress detection and segmentation

system

Filter Bank Based Pavement Distress Segmentation System

Image Restoration After Decompression and Synthesis

Filter Bank Based Pavement Distress Segmentation System

Post Detection & Segmentation Block

Filter Bank Based Pavement Distress Segmentation System

Two Standard Deviations Below The Mean Two Standard Deviations Below The Mean

Eliminate More Random Data

Filter Bank Based Pavement Distress Segmentation System

Why not use subband information?

Filter Bank Based Pavement Distress Segmentation System

Step Response using various lowpass filters

Blue-Low Delay filter results

Green-linear phase results

Red-High Delay filter results

Time-Varying Filter Bank Block

x = horizontal locationy = intensity

Filter Bank Based Pavement Distress Segmentation System

50 100 150 200 250 300 350 400 450 500

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Time-Varying Filter Bank Block Segment Results & Density Segmentation

Time-Varying Filter Bank MaskTime-Varying Filter Bank

Mask For Impulse

Filter Bank Based Pavement Distress Segmentation System

Weber Ratio∆ I/I≈0.2

Contrast Clustering Block

ResultsNo Compression Coder

Ground Truth Current Methods Enhanced Method

Conclusion & Future Work

Conclusion It is possible to design a filter bank based pavement distress

segmentation method that can be used for compressed and raw images.

We designed a system level filter bank based automated distress detection and segmentation system

Future Work Understand the properties of cracks in different material

types Develop a much more sophisticated method for performing

density segmentation