Thorough Analysis of Object Detection for Autonomous Vehicles

Autonomous vehicles (AVs) represent a transformative advancement in transportation, with object detection serving as a critical component for their safe and efficient operation. This paper provides a thorough analysis of object detection techniques tailored for autonomous vehicles, encompassing traditional methods, deep learning-based approaches, and emerging trends. We begin by examining classical techniques such as Haar cascades and Histogram of Oriented Gradients (HOG), highlighting their limitations in handling complex real-world scenarios. Subsequently, we delve into state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Region-based CNNs (R-CNNs), You Only Look Once (YOLO), and Single Shot Detectors (SSDs), evaluating their accuracy, speed, and robustness in diverse driving conditions. The study also explores the integration of sensor fusion techniques, combining data from cameras, LiDAR, and radar to enhance detection reliability. Challenges such as occlusions, adverse weather, and real-time processing constraints are discussed, along with potential solutions. Furthermore, we analyze the impact of dataset quality, annotation methods, and evaluation metrics on model performance. Finally, the paper outlines future directions, including the adoption of transformer-based architectures, edge computing, and continual learning for improved adaptability. This comprehensive review aims to guide researchers and practitioners in selecting and advancing object detection methodologies to meet the evolving demands of autonomous driving systems.