Lightweight vegetable detection algorithm based on improved YOLOv5

With the rapid development of deep learning, vegetable detection based on computer vision in smart supermarkets can save a lot of manpower. To solve the problem that the reported target detection algorithm fails to balance detection accuracy and model size well, a lightweight vegetable detection method that is based on yolov5 model was proposed in this work. The method firstly improved the detection accuracy by adding a fusion attention mechanism (Convolutional Block Attention Module) in the backbone part, and then reduces the model parameters by replacing the normal convolution in the network with Ghost convolution to ensure the accuracy at the same time. The accuracy of localization was improved by using Alpha-IoU as the loss function of the bounding box regression. The experimental results show that the improved method achieves an average accuracy of 92.5%, which is 3.2% higher than yolov5, and the total model parameters are 6.1 MB, which is 1.0 MB lower than yolov5, meeting the requirement of balancing detection accuracy and model size.