This paper provides a comprehensive overview of the Ultralytics YOLO (You Only Look Once) family of object detectors, focusing on architectural evolution, benchmarking, deployment perspectives, and future challenges. The latest release, YOLO26 (or YOLOv26), introduces key innovations such as Distribution Focal Loss (DFL) elimination, native NMS-free inference, Progressive Loss Balancing (ProgLoss), Small-Target-Aware Label Assignment (STAL), and the MuSGD optimizer for stable learning. YOLO11 introduced modules focused on hybrid task assignment and efficiency, YOLOv8 introduced separate detection heads and anchor-free prediction, and YOLOv5 introduced a modular PyTorch foundation that enables modern YOLO development. Using the MS COCO dataset as a benchmark, we perform quantitative comparisons of YOLOv5, YOLOv8, YOLO11, and YOLO26 (YOLOv26), as well as cross-comparisons with YOLOv12, YOLOv13, RT-DETR, and DEIM (DETR with Improved Matching). We analyze metrics such as precision, recall, F1 score, mean accuracy (mAP), and inference speed to highlight the tradeoffs between accuracy and efficiency. We discuss deployment and application perspectives in robotics, agriculture, surveillance, and manufacturing. Finally, we identify challenges and future directions, including limitations in dense scenes, hybrid CNN-Transformer integration, open vocabulary detection, and edge-aware learning approaches.
