Detection and Classification of Defective Hard Candies Based on Image Processing and Convolutional Neural Networks

Abstract

Defective hard candies are usually produced due to inadequate feeding or insufficient cooling during the candy production process. The human-based inspection strategy needs to be brought up to date with the rapid developments in the confectionery industry. In this paper, a detection and classification method for defective hard candies based on convolutional neural networks (CNNs) is proposed. First, the threshold_li method is used to distinguish between hard candy and background. Second, a segmentation algorithm based on concave point detection and ellipse fitting is used to split the adhesive hard candies. Finally, a classification model based on CNNs is constructed for defective hard candies. According to the types of defective hard candies, 2552 hard candies samples were collected; 70% were used for model training, 15% were used for validation, and 15% were used for testing. Defective hard candy classification models based on CNNs (Alexnet, Googlenet, VGG16, Resnet-18, Resnet34, Resnet50, MobileNetV2, and MnasNet0_5) were constructed and tested. The results show that the classification performances of these deep learning models are similar except MnasNet0_5 with the classification accuracy of 84.28%, and the Resnet50-based classification model is the best (98.71%). This research has certain theoretical reference significance for the intelligent classification of granular products.