Waste recycling is an important way of saving energy and materials in the production process. In general cases recyclable objects are mixed with unrecyclable objects, which raises a need for identification and classification. This paper proposes a convolutional neural network (CNN) model to complete both tasks. The model uses transfer learning from a pretrained Resnet-50 CNN to complete feature extraction. A subsequent fully connected layer for classification was trained on the augmented TrashNet dataset [1]. In the application, sliding-window is used for image segmentation in the pre-classification stage. In the post-classification stage, the labelled sample points are integrated with Gaussian Clustering to locate the object. The resulting model has achieved an overall detection rate of 48.4% in simulation and final classification accuracy of 92.4%.
@article{arxiv.2011.01353,
title = {Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering},
author = {Yuheng Wang and Wen Jie Zhao and Jiahui Xu and Raymond Hong},
journal= {arXiv preprint arXiv:2011.01353},
year = {2020}
}