Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Computer vision (CV) has emerged as a pivotal area of artificial intelligence (AI), enabling machines to perceive, analyze, and understand visual information from the world. Among the various CV applications, real-time object detection plays a critical role in domains such as autonomous vehicles, surveillance systems, healthcare, and robotics. This paper provides a comprehensive review of the current state-of-the-art in computer vision and real-time object detection, focusing on classical techniques, deep learning-based approaches, and their real-time deployment strategies. Furthermore, the paper examines the challenges associated with object detection, including speed, accuracy, occlusion handling, and dataset limitations. It also highlights recent advances in convolutional neural networks (CNNs), region-based approaches, single-shot detectors, and transformer-based models that enhance real-time performance. Finally, the paper discusses potential future directions, including edge computing integration, lightweight models, and multi-modal fusion for robust object detection.

KEYWORDS: Computer Vision, Real-Time Object Detection, Deep Learning, Convolutional Neural Networks, YOLO, SSD, Transformer, Autonomous Systems