Evolutionary trajectory and multifaceted applications of YOLO object detection algorithms

With the continuous advancement of deep learning, the field of object detection is also evolving. As a key research topic in this area, the YOLO (You Only Look Once) algorithm has seen multiple versions and improved variants emerge, achieving remarkable results and being widely applied in various practical scenarios. This paper presents a comprehensive survey of the YOLO algorithm, tracing its evolutionary trajectory from YOLOv1 to YOLOv10, highlighting the strengths and weaknesses of each version, organizing key datasets and evaluation metrics, and outlining significant advancements such as lightweight structure optimization, the integration of attention mechanisms, and innovations in the IOU loss function. Furthermore, it explores YOLO's applications in diverse domains like military, industrial, transportation, and biological fields, along with ongoing research endeavors. The YOLO algorithm holds enormous potential in various application domains.