Detection of dish waste degree based on image processing and convolutional neural networks. Issue 1 (20th July 2022)

• Record Type: Journal Article
• Authors: Pu, Jing; Zhu, Shiping; Miao, Yujie; Huang, Hua
• Is Part Of: Environmental progress & sustainable energy. Volume 42:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-20
• Subjects: convolutional neural network -- dish waste -- image processing -- waste degree; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.13942
• Languages: English
• ISSNs: 1944-7442

Abstract

Abstract: Many restaurants have a certain amount of food waste. The monitoring of food waste will help restaurants to eliminate some dishes with outrageous waste and reduce waste from the source. In view of this, this research proposed a method to detect waste of dishes through image processing and deep learning technology. According to the remaining quantity of the dishes, the collected dish images were preliminarily divided into six levels, which were used as sample labels, and then the image of the uneaten dishes and the image of the dishes after eating were stacked as the input of the network. Trained in the InceptionV3, Xception, and ResNet18 network models, we find that compared with the single image data as the input, the effect of stacking the two images data as the input was better. The accuracy of sample label recognition increased by 6.97%, 5.81%, and 4.1% respectively. Further analysis discovered that the sample that predicted wrong on the test set data, its true label, and the predicted wrong label were adjacent. Therefore, with the help of the probability vector output by the trained network model, the definition method of the level of dish waste degree and its corresponding accuracy metric standard was further given. Finally, the recognition accuracy of the best network structure InceptionV3 on the test set data can reach 98.47%.