  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%.
