Review of YOLO Methods for Universal Object Detection

As the first single-stage object detection algorithm in the era of deep learning, YOLO has sparked a wave of enthusiasm in the field of computer vision with its powerful and unique paradigm, and has become a milestone achievement in object detection algorithms. It is still a typical algorithm that achieves the best balance between speed and accuracy, and is widely used in industrial fields such as autonomous driving and intelligent vision systems. In the past eight years, driven by deep learning technology, YOLO methods have developed rapidly and have profound impact on the entire field of object detection. This paper conducts an in-depth investigation of the YOLO method related work from the perspective of technological evolution, comprehensively summarizing the innovation and contributions of each iteration from the initial YOLO v1 to the latest YOLO v9 and YOLO v10. Based on the significant technological improvements at different time points, the YOLO method is divided into four parts: early basic YOLO, standard version YOLO, standard improvement YOLO, and unique improvement YOLO. The unique perspectives of the improvement methods in each period are introduced in detail. In addition, the dataset and indicators for evaluating the YOLO method are summarized, and detailed experimental results of different versions of YOLO and different models of the same version of YOLO are collected. The development and changes of YOLO are summarized from both macro and micro levels. Through analysis, the differences and inherent connections in the development framework, backbone network architecture, and prior box usage among different versions of YOLO are revealed, emphasizing the importance of balancing speed and accuracy in YOLO. Finally, through systematic review, the future development trends of YOLO method is summarized. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.