Tyre Pressure Detection using CNN

Tyre Pressure Detection using CNN
Photo by Nikolay Ivanov for Lobster on Dribbble

This Model uses Images of car’s tyre to predict whether the tyre is Full or Flat or if no tyre is detected.

The Dataset contains 3 classes — :

1. Full Tyre

It contains Images of car Tyres who have proper air pressure and hence they are considered full. Here is an example of how a full tyre looks -:

Tyre Pressure Detection using CNN: Full Tyre
Full Tyre

2. Flat Tyre

These tyres don’t have enough air pressure so they are flat i.e. they need to be filled again and checked or else it may become the very reason of an accident. Here is an example of how a flat tyre looks -:

Tyre Pressure Detection using CNN: Flat Tyre
Flat Tyre

3. No- Tyre

These images represent the scenario when the image contains something else other than a tyre or it may not contain proper image of tyre. Here is an example of no- tyre class :-

Tyre Pressure Detection using CNN: No-Tyre
No-Tyre

Here is a demonstration of how to build this model using Tensorflow and Keras using Python.

Importing Libraries

Loading the Dataset

Preprocessing

Model Building

Model Fitting

Plotting Accuracy of Model

Plotting Loss of Model

Predictions

No Tyre
No Tyre

Full Tyre
Full Tyre

Full Tyre
Full Tyre

We can see that our Model is performing well which the Predictions are verifying also. Now we will save our Model.

Saving and deepCC

deepCC is a framework which converts the Deep Learning Models into an executable file which can be used in IOT devices like Arduino, MCU’s, etc.

Here is the link for the Notebook — Click Here

Thank you for reading this article, have a great day!

Credit: Tanmay Mishra

Also Read: Driver Drowsiness Detection using CNN

Related Posts