TYRE GUARD

AI

BINARY TYRE DEFECT CLASSIFICATION · DEEP LEARNING

Can a smartphone photo tell you if your tyre is safe?

We built an AI to answer that.

PROJECT PROPOSAL

COMPUTER VISION

MOBILENETV2

FASTAPI

STREAMLIT

TYRENET · MENDELEY DATA · BINARY CLASSIFICATION

Kazim Hussain Mohammed  ·  AI & Data Science Bootcamp

LE WAGON · OCT 2026

DEFECTIVE TYRE

PROBLEM STATEMENT

The Problem

OUR SOLUTION

Binary classifier: Defective (1) vs Non-Defective (0) — from a single uploaded photo

Invisible Danger

Tyre defects — cracks, bulges, worn tread — are nearly impossible to detect without expert knowledge or specialized tools.

No Accessible AI Tool

There is no consumer-grade AI solution that classifies tyre health from a simple smartphone photograph.

High Stakes

Tyre failure causes thousands of road accidents annually. Early, automated detection can directly save lives.

Dataset & Preprocessing

TYREGUARD AI · PROJECT PROPOSAL

TyreNet — Mendeley Data

Binary Labels: 0 = Non-Defective  ·  1 = Defective

X = Tyre Photographs

STEP 01

Inspect

Check labels, class balance, and corrupt files. Detect duplicates and near-duplicates to ensure a clean, reliable dataset before any processing begins.

Data Quality Gate

STEP 02

Split

70% train / 15% val / 15% test. Same-tyre photos are kept within a single split to prevent data leakage and maintain class balance across all sets.

70 / 15 / 15 Ratio

STEP 03

Resize

Convert all images to RGB and resize to 224×224 px with padding to preserve tyre proportions — the required input resolution for MobileNetV2.

224 × 224 px · RGB

STEP 04

Augment

Training-only: small rotations and brightness shifts to improve generalisation. Avoid transforms that erase cracks or introduce artificial defect patterns.

Training Only · No Fake Defects

Model Strategy

3 Models

Binary Classification

TensorFlow / Keras

MODEL 01

Reference Floor

Majority-Class

Baseline

No learning required

Complexity

None

Expected Accuracy

~50–60%

Always predicts most common training label

Establishes minimum performance reference

Zero trainable parameters

MODEL 02

From Scratch

Small CNN

From Scratch

Custom architecture

Complexity

Low–Medium

Expected Accuracy

~70–80%

Conv2D → Pool → Conv2D → Pool → GAP

Dropout → Sigmoid output layer

Learns directly from tyre images

MODEL 03

Transfer Learning

MobileNetV2

Transfer Learning

ImageNet pretrained

Complexity

High

Expected Accuracy

~90–95%

Freeze pretrained layers → train new head (lr 0.001)

Fine-tune later layers if validation justifies (lr 0.00001)

GAP → Dropout → Sigmoid · Early stopping + checkpoint

MAIN CANDIDATE

Loss: Binary Cross-Entropy

Optimizer: Adam

Batch Size: 32

Early Stopping + Best Checkpoint

Input: 224 × 224 RGB

Evaluation Strategy

MODEL ASSESSMENT

Recall — Priority Metric

SAFETY CRITICAL

Catch every defective tyre — false negatives are safety failures. Defective class is the positive class; missing a defect is the worst possible outcome and drives all threshold decisions.

Precision, F1 & Accuracy

Precision measures how many defect predictions are correct. F1 Score balances precision and recall into a single metric. Accuracy provides overall correctness alongside the other metrics.

Confusion Matrix

Reveals exactly which mistakes occur — especially missed defects (false negatives) that pose the greatest safety risk. Inspect visually to detect if the model learns backgrounds, lighting, or camera artefacts instead of tyre defects.

Validation → Test Split

Use validation data to select the model, tune settings, and choose the decision threshold. Evaluate the final configuration once only on the untouched test set to get an honest performance estimate.

Tech Stack & Architecture

PROJECT INFRASTRUCTURE

Data Layer

Data loading, inspection, and image preprocessing pipeline for tyre dataset preparation.

Python

Pandas

Pillow

LAYER 1

Training Layer

Model training pipeline with GPU-accelerated Jupyter environment, evaluation, and visualisation.

TensorFlow / Keras

tf.data

scikit-learn

Matplotlib

Jupyter

LAYER 2

Serving Layer

Prediction API loading the saved model at startup; Streamlit UI for photo upload, verdict, and confidence score.

FastAPI

Streamlit

Keras .keras

LAYER 3

Deployment Layer

Containerised packaging and cloud deployment with version control for team collaboration.

Docker

Google Cloud Run

GitHub

LAYER 4

PROJECT CONCLUSION

The Demo & What We Deliver

Upload a tyre photograph → receive a DEFECTIVE or NON-DEFECTIVE verdict with a clearly labelled confidence score and prototype limitations explained.

4 KEY DELIVERABLES

Preprocessing Pipeline

Reproducible end-to-end data preparation from raw images to model-ready tensors.

Trained Models

Baseline CNN vs MobileNetV2 transfer learning— full comparison with metrics.

Deployed Web App

FastAPI backend + Streamlit UI — upload image, see prediction and score live.

Evaluation Report

Recall, Precision, F1, Confusion Matrix —defective-class recall as priority metric.

Our commitment: A tyre-image classifier trained on the Mendeley dataset, comparing CNN from scratch with MobileNetV2 transfer learning — evaluated on defective-class recall and deployed through a reproducible, end-to-end image-upload application .

LIVE DEMO

DEMO FLOW

Upload Photo

›

Model Inference

›

DEFECTIVE

MODEL CONFIDENCE SCORE

91.4%