Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering
This work addresses waste recycling efficiency for environmental applications, but it is incremental as it combines existing methods like ResNet-50 and Gaussian clustering on a known dataset.
The paper tackled the problem of identifying and classifying recyclable waste from mixed objects using a CNN model with transfer learning and Gaussian clustering, achieving a detection rate of 48.4% and classification accuracy of 92.4%.
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%.