A Comprehensive Review Of Yolov26 And Existing Yolo Variants: Architecture, Training Paradigms, And Applications In Medical And General Computer Vision

The YOLO family of detectors has been widely adopted for real-time computer vision due to their unified single-stage design and efficient inference. This work thoroughly reviews state-of-the-art YOLO models, focusing on the main ideas of YOLOv26-an end-to-end detection framework that removes nonmaximum suppression from the whole inference pipeline. Besides, its architectural formulation is investigated in terms of the detection head, the loss design without Distribution Focal Loss, unified training objective, and MuSGD-based optimization. Then, this work further compares YOLOv8 to YOLOv26 along several axes: architectural evolution, multi-task support, latency, and deployment characteristics. Various benchmark results show that YOLOv26 strikes a balance between competitive detection accuracy and substantial CPU inference latency reductions, especially under resource-constrained conditions. Moreover, this work has conducted a review of recent applications of the YOLO model across different domains, including medical imaging, surveillance, autonomous systems, agriculture, and industrial vision, and discussed persistent challenges, including small-object detection, robustness, and domain generalization.