AI Based Road Inspection System for Mexico

Local Chapter Mexico City, Mexico Chapter

Coordinated byMexico ,

Status: Completed

Project Duration: 08 Apr 2023 - 05 Jun 2023

Open Source resources available from this project

Project background.

In the cities of Mexico there is a very important road problem, which affects from travel times to the quality of life of the inhabitants, not to mention the mechanical affectations of vehicles that fall into the holes or sinkholes.

That is why we chose to replicate the challenge of “AI Based Road Inspection System” of the UAE chapter in Mexico.

Mexico is a country that has adopted the 2030 agenda in its political roadmap, so we see a direct impact of this project on goals 9 (Industry, innovation and infrastructure) and 11 (Sustainable cities and communities). The idea is to make this project transcendent for the inhabitants of the country and the community in general.

The problem.

Current practices of performing road inspections are time-consuming and labour-intensive. Road surfaces degrade on a daily basis as a result of the heavy traffic on them. This will not only impact the driver’s comfort but will also impact economic efficiency. To maintain roads as efficiently as possible, municipalities perform regular inspections. The project aims to use machine learning to study and analyze different types of road defects and automatically detect any road abnormalities. We will design, build and test an inspection system for this purpose. The system is equipped with a camera to collect video streams from different roads with and without defects. Then, the captured data will be analyzed using the Matlab machine learning toolbox to train and test the network. Finally, the system will provide recommended actions for the municipality related to actions required to fix/correct the road defects. The approach is divided into 3 main tasks: Data acquisition, Data Training/Testing, and Dashboard Building and Testing.

– Data acquisition stage: In this stage, we will use HD cameras to capture live videos of different road defects and issues. We will also collect both images from standard datasets, images from real roads, and live video recordings.
– Data Training/Testing: Collecting and labeling roads are often tedious and many times require expert knowledge. Therefore, we decided to use transfer learning to address challenges related to the scarcity of data and lack of human labels. Matlab machine learning toolbox will be used to classify road defects. We will also use standard image processing techniques to highlight areas and guide the inspection process
– Dashboard Building and Testing: The dashboard or the graphical interface will visualize the defects and the recommendation to the municipality.

Project goals.

The goal of this work is to investigate the ability of various machine learning classifiers to detect road defects with the highest possible accuracy, as well as to build a Dashboard to visualize detected road defects.This project aims to: - Automate the inspection process to reduce time and effort for better efficiency - Collect as much data as possible about UAE roads (Cracks, Patching, Rutting, and deformation) - Apply machine learning algorithms and image processing techniques to detect various road defects. - Classify images using a pre-trained deep convolutional neural network (e.g., GoogLeNet, squeeznetm...etc.). - Visualize road defects in real time using dashboards/graphical user interface - Reporting the machine learning approach

Project plan.

  • Week 1

    – Research
    – Data Collection

  • Week 2

    – Data Pre-Processing

  • Week 3

    – Prepare/ Explore ML approaches

  • Week 4

    – Explore the pre-trained network and ML models

  • Week 5

    – CNN training and validation 3.3 Performing transfer learning

  • Week 6

    -System testing and accuracy reporting 4.2 Building Dashboard to visualize the output/ Deployment

  • Week 7

    – Generate final report and recommendation/ Evaluate Model accuracy

  • Week 8

    – System testing and accuracy reporting 4.2 Building Dashboard to visualize the output/ Deployment and Generate final report and recommendation/ Evaluate Model accuracy

Learning outcomes.

During this project, participants will be mainly able to:
– Perform data collection and pre-processing for road images
– Investigate the current pre-trained convolutional neural network, the Deep Learning Toolbox, and transfer learning
– Apply several machine learning models to classify road images
– Build a Dashboard to visualize detected road defects also teamwork, Research, Data Analysis, and Machine Learning Models

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