Food Image Recognition and Food Safety Detection Method Based on Deep Learning

Year: 2021

Type: article

Abstract: With the development of machine learning, as a branch of machine learning, deep learning has been applied in many fields such as image recognition, image segmentation, video segmentation, and so on. In recent years, deep learning has also been gradually applied to food recognition. However, in the field of food recognition, the degree of complexity is high, the situation is complex, and the accuracy and speed of recognition are worrying. This paper tries to solve the above problems and proposes a food image recognition method based on neural network. Combining Tiny-YOLO and twin network, this method proposes a two-stage learning mode of YOLO-SIMM and designs two versions of YOLO-SiamV1 and YOLO-SiamV2. Through experiments, this method has a general recognition accuracy. However, there is no need for manual marking, and it has a good development prospect in practical popularization and application. In addition, a method for foreign body detection and recognition in food is proposed. This method can effectively separate foreign body from food by threshold segmentation technology. Experimental results show that this method can effectively distinguish desiccant from foreign matter and achieve the desired effect.

Source: Computational Intelligence and Neuroscience

DOI: 10.1155/2021/1268453

Authors: Ying Wang, Jianbo Wu, Hui Deng, Xianghui Zeng

Institutions: Shaoyang University, Nankai University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $2,550