Created using Colab

[Shibika-Roy]

"Hybrid Terrain Classification: Added CNN with Sensor Fusion using TensorFlow Keras"
Overview:
This pull request introduces the following enhancements to the terrain classification system:

CNN-based Image Classification:

Added a Convolutional Neural Network (CNN) for terrain classification from input images. The model processes 64x64 RGB images and includes two convolutional layers followed by dense layers for feature extraction.
Sensor Fusion:

Integrated sensor data (e.g., accelerometer, gyroscope) with the CNN model using fully connected layers to enhance classification accuracy. This approach helps the model consider not only visual features but also physical measurements for terrain classification.
Edge Computing Optimizations:

Prepared the model for edge computing deployment by adding TensorFlow Lite (TFLite) export functionality. The model is now optimized for running on low-resource devices like drones and robots for real-time terrain recognition in resource-constrained environments.
Testing:

Created dummy data (image and sensor data) for testing purposes to validate that the fusion of CNN and sensor data works effectively.

[Shibika-Roy]

please level this as gssoc extd 2024 and hactober fest acceptd

[Shibika-Roy]

https://colab.research.google.com/drive/1cV5Q0hpB-MvO9hShSdYd4VudCn-sxMdU?usp=sharing

[Akasxh]

Let me review it, I'll merge it once I've been through or if there are any issues.

[Akasxh]

What specific benefits were you hoping to achieve by adding this separate sensor model?

And there was no use of the dataset, I recommend training it on the dataset and submit along accuracy and loss over training.

[Akasxh]

Your sensor model does not have anything unique, please read some papers/tutorial on hybrid and multimodality.