Research And Implementation Of Apple Appearance Nondestructive Detection System Based On Deep Learning

As a major country in apple productions,China produces apples accounting for more than 50%of the global productions.As the outward appearances of apples exert a significant influence on their sales speed,price and benefit,it is a key part to conduct rapid and accurate detection and classification of them in pre-sale processing.At present,the appearance detection of apples still employs traditional method,which features repeatability and inefficiency.In view of this,the paper proposes a method of non-destructive detection of apple appearance based on deep learning models by obtaining the data set through pictures of apple appearance first,marking their quality artificially,and finally proceeding to learning modeling by using Faster R-CNN,the deep learning algorithm.Experiments show that the method in this paper,with high detection accuracy,is able to achieve the expected design purpose.The following is the main work of this paper:1.In this paper,the shortcomings of traditional target detection methods were briefly introduced,the basis of neural network and the process of target detection algorithm were introduced,and a deep learning Faster R-CNN algorithm was proposed to complete the apple appearance nondestructive target detection.2.In this paper,different kinds of nondestructive and destructive apple images were collected under complex environment to make a specific COCO dataset.and a Faster R-CNN apple appearance nondestructive detection system based on VGG16 backbone network model was completed under the self-made dataset.3.Based on the detection results,this paper puts forward the reasons that may affect the low precision of target detection algorithm.Through the analysis of VGG16 and ResNet50 network model structure and network model contrast experiment,summarized the factors affecting the performance of network model.4.In order to improve the detection accuracy,this paper adopted methods such as adjusting model parameters,expanding the dataset and modifying the trunk network model to ResNet50 to test the apple appearance nondestructive detection system.The average accuracy of apple’s appearance detection system improved by 11.4%,and the average detection accuracy of the final experiment reached 86.4%.