A Preliminary Analysis of Research Hotspots and Evolution Trends of YOLO Object Detection Algorithm Based on Bibliometrics

Abstract

Publications related to YOLO have increased rapidly year by year, reflecting a robust development momentum of this research field. Based on reference co-citation and keyword co-occurrence analyses, YOLO research inherits the traditional theoretical framework of object detection and has formed a complete research system covering theoretical exploration, algorithm iteration, performance optimization and practical application. Current research hotspots mainly include network lightweight design, small object detection, feature fusion and attention mechanism optimization, which are widely applied in industrial inspection, remote sensing, agriculture and other fields. This paper briefly sorts out the research context in this field, which can provide a reference for the subsequent optimization and application research of the YOLO algorithm.