Express packaging defects detection model based on YOLOv8

This paper proposes the SFN-YOLOv8 detection model for the detection of express packaging defects. This model has the following characteristics: it uses lightweight ShuffleNetV2 as the backbone network of the model and adopts deformable convolution (DCN), thus reducing the number of model parameters. The C2F-CPCA module is proposed in the neck part to enhance the expressive ability of the network in feature representation. Wiou is used in the loss function to reduce the impact of large gradients or harmful gradients appearing in extreme samples in the data set on the loss calculation. Experimental results indicate that compared with the initial model, the SFN-YOLOv8 model has reduced its parameters by 0.15M, decreased GFLOPs by 0.8G, and improved [email protected] by 1.9%.