From YOLOv5 to YOLOv8: Structural Innovations and Performance Improvements

With the rapid advancement of object detection technology, the YOLO series has become ubiquitous across diverse computer vision applications owing to its efficiency and real-time capabilities. This paper delivers a systematic comparative analysis of YOLOv5 and YOLOv8, with an emphasis on their innovations and distinctions in network architecture, training mechanisms, inference optimizations, and detection performance. Relative to YOLOv5, YOLOv8 introduces substantial structural enhancements, notably the lightweight C2f feature extraction module and an anchor-free detection head, alongside state-of-the-art data augmentation strategies and novel loss functions that collectively boost both accuracy and inference speed. Furthermore, YOLOv8 advances inference optimization by supporting more flexible model export formats and acceleration pipelines, thereby facilitating deployment on mobile and edge devices. Through this comparison, we trace the technological evolution from YOLOv5 to YOLOv8 and project future trends in object detection research—particularly the integration of emerging techniques to further elevate model efficiency and performance. Our findings underscore the enduring potential of the YOLO series to drive progress in object detection methodologies.