Survey of Development of YOLO Object Detection Algorithms

In recent years, deep learning-based object detection algorithms have been a hot topic in computer vision research, with the YOLO (you only look once) algorithm standing out as an excellent object detection algorithm. The evolution of its network architecture has played a crucial role in improving detection speed and accuracy. This paper conducts a comprehensive horizontal analysis of the overall frameworks of YOLOv1 to YOLOv9, comparing  the network architecture (backbone network, neck layers and head layers) and loss functions. The strengths and limitations of different improvement methods are thoroughly discussed, with a specific evaluation of the impact of these improvements on model accuracy. This paper also delves into discussions on dataset selection and construction methods, the rationale behind choosing different evaluation metrics, and their applicability and limitations in various application scenarios. It further explores specific improvement methods for YOLO algorithm in five application domains (industrial, transportation, remote sensing, agriculture, biology), and discusses the balance among detection speed, accuracy, and complexity in these application domains. Finally, this paper analyzes the current development status of YOLO in various fields, summarizes existing issues in YOLO algorithm research through specific examples, and in conjunction with the trends in application domains, provides an outlook on the future of the YOLO algorithm. It also offers detailed explanations for four future research directions of YOLO (multi-task learning, edge computing, multimodal integration, virtual and augmented reality technology).