Enhancing Terrain Recognition through Data Augmentation and Depth Analysis

[ChitteshKumar]

Problem Description:

Terrain recognition plays a crucial role in various applications, including autonomous driving, robotics, and environmental monitoring. However, current terrain recognition models often struggle with overfitting, limited generalization, and sensitivity to varying terrain conditions. This proposal aims to enhance the accuracy and robustness of terrain recognition systems by employing advanced techniques such as data augmentation, depth analysis and stereo vision using OpenCV.

Model Description:

To improve terrain recognition, I propose the following methodologies:

• Data Augmentation: Implement data augmentation techniques to artificially increase the diversity of the training dataset. This will include transformations like rotation, flipping, scaling, and brightness adjustments to create variations of existing images. This approach helps prevent overfitting and enables the model to generalize better to unseen data.

• Depth Analysis and Stereo Vision with OpenCV: Integrate depth analysis techniques using OpenCV to extract depth information from terrain images. This can help identify and differentiate terrain features based on their spatial relationships, enhancing the model's understanding of terrain characteristics.

Experimental Optimization:
Conduct experiments with different hyperparameters, including the number of epochs, various optimizers (e.g., Adam, SGD), and dropout values to determine the optimal settings for the model. This systematic approach will enable us to identify the configurations that yield the best performance in terms of accuracy and generalization.

Estimated Time for Completion: I estimate that it will take approximately 1-2 weeks to implement these enhancements.

Expected Outcome: Upon implementation, the enhanced terrain recognition model is expected to demonstrate improved accuracy and robustness against varying terrain conditions.

I am enthusiastic about the opportunity to contribute to this project and improve terrain recognition capabilities using these advanced techniques.

Thank you for considering this proposal. I look forward to your feedback and support. @Akasxh

[Akasxh]

This sounds interesting! I will assign this issue to you.

Also make sure to include code explainability with a seperate readme and keep updating as you proceed. While trying multiple techniques, do take screenshots of model performance and save the history of those models so as to see how you have proceeded eventually.

Not needed but try optimising the model to be as lightweight as possible!

[AKSHITHA-CHILUKA]

@Akasxh despite of many times telling you have not changed the labels.
The crt labels are level3 with no spaces , gssoc-ext is the label not gssoc24-extd , hactoberfest-accepted is the label not hactoberfest .
I hope you will change all the label's in all the issues and pr's as soon as possible .

[ChitteshKumar]

I have started working on this issue, however, I cannot see the Test and Train Data passed to the CNN model from V3 file. I am getting the following error.

[Akasxh]

Hi what error did you exactly encounter?

[Akasxh]

I can not see what error you are exactly experiencing.

[ChitteshKumar]

I can not see what error you are exactly experiencing.

Basically I cannot see the dataset available in the repo I have forked so the error is the file path of test and train data.