Vegetable image recognition algorithm based on Swin transformer

Abstract

Target detection techniques based on computer vision can be used for vegetable recognition and classification, which can effectively avoid the time-consuming and labor-intensive problems faced by manual operations at the large supermarket checkout lanes and produce market. In order to accurately recognize vegetable image, an improved Faster R-CNN recognition technique based on the Swin Transformer is proposed in this paper. The backbone network is replaced by the Swin Transformer to improve the accuracy and efficiency of feature extraction. The ROI Align is employed to protect the integrity of image data. Furthermore, the more effective GIoU loss function is used to simplify the training process in order to reduce the computational resources and time consuming during training. Finally, the experimental results show that the proposed algorithm has an accuracy improvement of 6.1% compared with the previous methods.