Research On Detection And Removal Technology Of Foreign Matter In Quick-frozen Food

Quick-frozen food is popular because of its advantages of freshness,hygiene,reasonable nutrition and convenient eating.Quick-frozen dumplings with Chinese characteristics are welcomed by the domestic and foreign markets,and the output is increasing rapidly.However,in recent years,food safety incidents caused by foreign materials such as metal,stone and glass have seriously damaged consumers' pysical and mental health.Therefore,foreign body detection of frozen food is of great significance to ensure food safety.In view of the fact that metal detectors can only detect metals and the results can not be visualized intuitively,this paper used X-ray imaging technology and image processing technology to automatically detect and classify five foreign bodies in dumplings,including metal balls,fine wires,screws,stones and glass.The main research contents include:(1)The X-ray imaging process and detection principle were analyzed,and a rotating roller eliminating device was designed to eliminate foreign body dumplings.In view of the fact that the original X-ray dumpling image contains a lot of noise and low contrast,we used mean filtering,median filtering,bilateral filtering,logarithmic transformation,gamma transformation,contrast stretching transformation to denoise and enhance the original image.According to the MSE,PSNR and SSIM values,the processed image quality is evaluated.We finally chose 3×3 median filter and contrast stretch transformation as the preprocessing method in this paper.(2)According to the distribution characteristics of X-ray dumpling images and the problem that several conventional segmentation algorithms can not effectively segment foreign bodies in dumplings,a threshold segmentation algorithm combining the maximum entropy algorithm with additional offset and the particle swarm optimization algorithm with linear decreasing weight was proposed: an offset function was added to the entropy of the image target area,and the total entropy of the dumpling image was used as the fitness function of the particle swarm algorithm to obtain the optimal segmentation threshold of the image.The experimental results showed that the proposed algorithm can effectively segment different foreign objects from the dumpling image and solve the problem quickly.(3)The LBP,HOG and Gabor texture features of the image were extracted and SVM was used to identify foreign and non-foreign dumplings.To further improve the recognition rate of foreign dumplings,an improved foreign dumpling recognition method based on LeNet-5 convolutional neural network model(CNN)was proposed: the batch normalization layer and the Dropout layer was added to in the design of the network structure to improve the network learning speed and avoid over-fitting of the network,and Softmax linear regression classifier,ReLu as activation function and Max-Pooling as downsampling method are used to optimize,train and validate the designed CNN model;In order to facilitate the second processing of dumplings,the roundness,aspect ratio,eccentricity of the foreign object in the binary image of the dumplings,and the gray mean,entropy,the gray invariant moments,the LBP features of the minimum cir-cumscribed rectangular area of the foreign bodies in the gray image were extracted to construct feature vectors,and then used BP neural network to classify foreign bodies.(4)A set of X-ray boxed dumpling detection system based on MATLAB was developed.The system realized the basic functions of image processing and analysis and the automatic judgment function of foreign objects.In this paper,X-ray detection technology is applied to detect foreign bodies in boxed dumplings,which can successfully identify various foreign bodies in dumplings,which has important practical significance for ensuring food safety.