Candy classification using convolutional neural networks, data augmentation and transfer learning: Application and a new public dataset

Quality control is an important task in confectionery because industrially produced candies are prone to suffer from a number of defects, such as cracks, crazing, unacceptable sizes and irregular shapes. Therefore, categorizing the candies into two classes (Non-Defective and Defective) is a key issue in the manufacturing process for the subsequent stages of packaging, pricing and selling as either first-quality products or second-quality ones. According to that motivation, we present and application of deep learning techniques -namely using convolutional neural networks, data augmentation and transfer learning- to address the problem of image-based classification of manufactured candies. A comprehensive experimental evaluation was performed considering several variants for both the deep learning techniques and the set of images. Besides, a new dataset of labeled images of manufactured candies has been built and released for public usage.