Garbage classification recognition based on improved YOLOv8 model

The use of intelligent garbage classification methods and devices for waste sorting is set to become the future trend in waste management. With the development of high-precision image recognition algorithms, research in intelligent garbage classification has gradually matured. The core lies in utilizing image recognition technology to analyze garbage image information, thereby surpassing the limitations of manual sorting and achieving higher standards of speed and accuracy. This paper proposes a target detection scheme based on the introduction of the C2f_RepGhost module into YOLOv8, aiming to construct a model system for efficient garbage identification and classification. Thanks to its streamlined design and real-time response characteristics, this model can operate efficiently on platforms such as smart mobile devices, enabling real-time intelligent automatic classification of various types of waste.