Abstract:
Abstract： The current garbage classification and detection algorithms based on deep learning often have a large number of model parameters， leading to increased storage and computing costs. This results in significant computational load when running on resource-constrained mobile devices. To solve the above problems， a lightweight garbage detection algorithm based on improved YOLOv8n is proposed. The improved algorithm uses the GhostNet convolution module to realize the lightweight network in the YOLOv8n feature extraction network module. The RepConv structure reparameterization is used to improve the backbone network， which enhances the backbone network’s feature extraction ability and reduces its complexity during inference stages. Additionally， the C2f module of the neck network is improved by using convolution kernels of different sizes to obtain multi-scale feature information， thereby enhancing the detection accuracy of the model. Finally， transfer learning is used to improve generalization capabilities while accelerating model training for better overall detection accuracy. The experimental results show that the improved algorithm reduces both parameter count and computation by 26.8% and 24.7%， respectively， compared with the original model while achieving average detection accuracies of mAP50 and mAP50：95 at 98.1% and 93.8%. Overall， the proposed method not only reduces model complexity but also has better detection accuracy and can better adapt to the requirements of mobile devices.

Key words: Key words： garbage classification and detection； lightweight network； Ghost convlution； structure reparameterization； multi-scale feature fusion ,