Advancements in Deep Learning for Slam in Autonomous Vehicles: Challenges and Solutions

This project presents the development and implementation of a Convolutional Neural
Network (CNN) for vehicle detection, integrating advanced machine learning techniques with
practical applications in computer vision. The primary objective was to design a robust model
capable of accurately classifying images as either containing vehicles or not. The project
utilized a diverse dataset of vehicle and non-vehicle images, which were pre-processed to
enhance model performance.
The experimental setup included the use of Python as the primary programming language,
supplemented by powerful libraries such as TensorFlow for model training, OpenCV for
image processing, and Matplotlib for data visualization. The CNN architecture was defined
and trained on a substantial dataset, achieving a remarkable accuracy of 99.07% on the test
set. The training history was meticulously plotted, illustrating the model's learning curve and
validating its capability to generalize well to unseen data.