This repository contains two deep learning models based on the ResNet-18 architecture, specifically designed for intelligent vehicle applications. The models enable an autonomous vehicle to perform two key tasks: line following and obstacle avoidance. These models are trained to process visual input from a camera and make real-time decisions to navigate efficiently and safely.
The models are trained on custom datasets collected from real-world scenarios. The datasets include:
The datasets are preprocessed to ensure robustness and generalization across different environments.
To integrate these models into your intelligent vehicle system, you can use the following steps:
if you need to retrain or fine-tune the models,
you can use the Caffe framework along with the provided prototxt training configuration files.
This repository is licensed under the
MIT License. You are free to use, modify, and distribute the code and models for any purpose.
