• Mizanur Rashid
• Abdullah Ibne Sayed
• Md Masud Rana

In the field of computer vision, object detection technology has garnered significant attention in recent years due to its wide-ranging applications. Among the various detection algorithms, You Only Look Once (YOLO) stands out as a pioneering approach that formulates object detection as a regression problem, enabling end-to-end training and inference. This unique methodology ensures an optimal balance between speed and accuracy, making YOLO a preferred choice for real-time applications. Over the years, the YOLO series algorithms have evolved considerably, demonstrating remarkable success across diverse domains such as autonomous driving, surveillance, robotics, and medical imaging. This paper provides a comprehensive investigation into the critical applications of YOLO algorithms, highlighting their practical implementations and impact. Furthermore, a detailed comparison is drawn between YOLO and other state-of-the-art object detection frameworks, emphasizing its advantages in terms of computational efficiency, scalability, and adaptability. Based on this analysis, the distinctive characteristics of YOLO-such as its unified detection pipeline, multi-scale feature learning, and lightweight variants-are systematically summarized. Finally, the study explores potential future directions for YOLO, including enhancements in small-object detection, robustness in occluded scenarios, and integration with emerging paradigms like transformer-based architectures. By synthesizing these insights, this review aims to serve as a valuable reference for researchers and practitioners seeking to leverage YOLO’s capabilities for next-generation vision systems.

YOLO, neural network, object detection and recognition.

"A Comprehensive Review and Performance Analysis of YOLO Architectures for Real-Time Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.a187-a192, October-2025, Available :http://www.jetir.org/papers/JETIR2510022.pdf

"A Comprehensive Review and Performance Analysis of YOLO Architectures for Real-Time Object Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppa187-a192, October-2025, Available at : http://www.jetir.org/papers/JETIR2510022.pdf