Improved Asian food object detection algorithm based on YOLOv5

Abstract

An improved model called TR-YOLO is employed for Asian food object detection. Firstly, the ViT module is introduced into the model to make better use of global features. Secondly, the Swin Transformer module is introduced on the three detection branches to output the features. Finally, the Mconcat feature fusion method is proposed, which enables the model to learn the feature weights to assign feature channels independently. The experimental results show that the TR-YOLO model further improves the detection accuracy.