Summary of Brijesh Pandya's Paper Presentation on Robotics Application for Road Crack Detection

Introduction

India has the second-largest road network globally, playing a crucial role in transportation. However, traditional methods of assessing road conditions are inefficient and subjective. Integrating robotics and machine learning can provide more accurate results. This research focuses on an Autonomous Robot System (ARS) designed to evaluate pavement conditions in India, utilizing Convolutional Neural Networks (CNNs) for crack identification.

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Research Objective

The research aims to develop an ARS, establish a road condition data collection methodology, and create an algorithm for pavement evaluation. The study addresses the need for a tailored approach to road evaluation in India, leveraging advanced technology for precise and consistent results.

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Research Methodology

The research developed an ARS and Python algorithm for data collection and pavement defect identification. The algorithm underwent stages of testing, flaw identification refinement, image processing optimization, and accuracy assessment. The rover development included chassis construction, top frame assembly, and an ultrasonic sensor system for distance measurement.

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