You will get AI Road Inspection: Pavement Crack Detection | Computer Vision + OpenCV
Project details
I will build an automated pavement distress detection system using deep learning (CNNs) and OpenCV to identify road cracks and structural defects from image data, reducing manual inspection costs and improving maintenance planning.
You will get:
✅ Custom CNN Model trained on road surface images
✅ Image Preprocessing Pipeline with OpenCV
✅ Defect Detection Algorithm identifying crack types/sizes
✅ Web/Mobile Interface for image upload and analysis
✅ Detailed Analysis Reports with severity ratings
✅ API Endpoint for batch processing
✅ Model Training Code & Weights
✅ Performance Validation on test datasets
✅ Deployment Guide for cloud/on-premise
✅ 1-month technical support
You will get:
✅ Custom CNN Model trained on road surface images
✅ Image Preprocessing Pipeline with OpenCV
✅ Defect Detection Algorithm identifying crack types/sizes
✅ Web/Mobile Interface for image upload and analysis
✅ Detailed Analysis Reports with severity ratings
✅ API Endpoint for batch processing
✅ Model Training Code & Weights
✅ Performance Validation on test datasets
✅ Deployment Guide for cloud/on-premise
✅ 1-month technical support
Machine Learning Tools
Microsoft Excel, pandas, Python, PyTorch, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$200
|
Standard
$300
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 10 days | 30 days | 70 days |
Number of Revisions | 3 | 0 | 0 |
Number of Graphs/Charts | 7 | 15 | 20 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
About Femi
Full-stack developer || Machine learning engineer || web & App Design
Lagos, Nigeria - 1:04 am local time
My expertise includes:
Full Stack Development with Django, React, Node.js, and REST APIs
Machine Learning & AI using PyTorch, TensorFlow, and Scikit-learn
Computer Vision for automated detection and analysis (OpenCV, CNNs)
DevOps & Deployment with CI/CD, and GitHub Actions
Project Leadership & Technical Training
I have successfully delivered projects ranging from educational platforms and fintech solutions to AI-powered medical and infrastructure systems. I am passionate about creating efficient, secure, and scalable digital solutions that drive real-world impact.
Let’s collaborate to turn your ideas into robust, high-performance software.
Steps for completing your project
After purchasing the project, send requirements so Femi can start the project.
Delivery time starts when Femi receives requirements from you.
Femi works on your project following the steps below.
Revisions may occur after the delivery date.
Computer Vision Requirements & Data Collection
Define defect types to detect (cracks, potholes, etc.) Analyze available road image datasets (2,000+ images) Plan data augmentation strategies for model training Define accuracy targets (94%+ for crack detection)
Image Preprocessing Pipeline Development
Implement image cleaning and normalization Develop augmentation techniques (rotation, scaling, lighting) Create image segmentation for defect isolation Build batch processing for large image datasets