Fried food detection system based on symbiotic two-stream convolutional network and digital image

Abstract

The invention discloses a fried food detection system based on a symbiotic double-stream convolutional network and a digital image, including an image preprocessing module, a fast identification module, a classification and positioning module, a target cutting module and an image analysis module connected in sequence; an image preprocessing module Perform image stylization migration and image filtering processing on the input image in turn to obtain image tensors available to the network; in the fast recognition module, the image is quickly classified by a fully convolutional network composed of a co-occurrence feature extraction network and a recognition network; the classification and positioning module It is a fully convolutional network; the target cropping module uses the optimal frame to cut the target image from the original image; the image analysis module analyzes the target image and gives quantitative analysis results. The invention combines the symbiotic double-stream convolutional network and digital image analysis, and can realize fast and accurate location and attribute identification of fried food.

Description

technical field [0001] The invention belongs to the field of computer vision and image processing food detection, in particular to a fried food detection system combined with symbiotic double-stream convolution network and digital image analysis technology. Background technique [0002] Fried food is food that people eat daily, such as fried French fries, potato chips, chicken legs, chicken wings, etc. Whether it is a small or medium-sized restaurant, or a large-scale food production workshop or canteen, the fried food is mainly sorted and packaged manually at present; in a high-temperature environment, it is necessary to ensure food hygiene and fast sorting and packaging. Food production enterprises facing increasing labor costs have brought great challenges. Therefore, the study of automatic identification of such fried foods is of great significance to realize the automation of fried foods sorting work in complex environments. [0003] For general sorting work automation...

AI Technical Summary

Problems solved by technology

[0008] (3) As we all know, the training of neural networks requires a large amount of data, and the application scenarios of some detection systems cannot provide sufficient data information such as the detection problem in the food field involved in the present invention

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