A Detailed Review of YOLO Algorithms: YOLOv1 to YOLOv11

YOLO is a real-time object detection (OD) algorithm with convolution layers that performs OD and classification directly at the image level. Due to the operations performed at the image level, it works very fast compared to its peers. The YOLO algorithm, which is frequently used in both academic and industrial studies thanks to its speed-performance balance, is systematically analyzed from YOLOv1 to YOLOv11 in this study. In the study, the structural components of the algorithms are analyzed and a clear and simple architecture is drawn. Innovations and improvements are mentioned, and the development of the series is explained step by step. Object detection scenarios realized with YOLO in the literature are reviewed and organized into topics. In addition, the performance results in the original papers are compared based on co-metrics and datasets, and the development of the series is comprehensively analyzed. In the last stage, the projected areas of improvement and potential research areas for further development are discussed, and recommendations for researchers are presented.

Cite this article as: A. B. Varol Malko.oğlu and R. Şamlı, “A detailed review of YOLO algorithms: YOLOv1 to YOLOv11,” Electrica, 26, 0047, 2026. doi:10.5152/electrica.2026.26047.