Machine Learning Algorithms for Object Detection and Recognition in Autonomous Vehicles

Abstract

This paper explores the application of machine learning algorithms in the domain of object detection and recognition within autonomous vehicles. The study reviews current methodologies, evaluates their effectiveness, and identifies challenges and future directions. By analyzing various algorithms, including convolutional neural networks (CNNs), region-based CNNs (R-CNNs), and transformer-based approaches, this paper provides insights into their performance in real-world scenarios.