Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering

Abstract-Waste recycling is an important way of saving This study uses transfer learning from a pre-trained Resnet-energy and materials in the production process. In general 50 model to generate a model which is capable of classifying cases recyclable objects are mixed with unrecyclable objects, images of individual waste objects into the following six which raises a need for identification and classification. To paper proposes a convolutional neural network (CNN) model integrate the model into actual application, which often deals to complete both tasks. The model uses transfer learning with bird's-eye view of piles of waste, a sliding-window process from a pretrained Resnet-50 CNN to complete feature in the pre-classification stage split the image into smaller extraction. A subsequent fully connected layer for fragments for the CNN to process, and the labelled points are classification was trained on the augmented TrashNet dataset integrated with Gaussian Mixture Model in the postclassification [1].