Application of object recognition technology with neural network for a food production line management system —Improving identification accuracy

Abstract

In a production line management of food factories, a worker performs pre-shipment visual inspection on product images. In order to save the labor cost, we have developed a service to identify the meat type (e.g., barbecue, shabu-shabu, mince etc.) of a packed meat image using ResNet, as well as the merchandising label on the package using a combination of YOLO and ResNet. We, however, had a few errors when identifying for packages of different meat. By applying Grad-CAM to the identification model, we have found out that the model focuses on the merchandising label instead of the meat itself. Thus, we fixed our model by removing the label area on the training images, and it can now identify the meat type even on packages of different meat.