Image Dataset for Visual Objects Classification in 3D Printing
This addresses security risks in additive manufacturing for 3D printing users and industries, but is incremental as it applies existing deep learning methods to a new dataset.
The researchers tackled the problem of detecting illegal components like guns in 3D printing by creating a dataset of 61,340 2D images across 10 classes and achieved 98.16% classification accuracy using a CNN model.
The rapid development in additive manufacturing (AM), also known as 3D printing, has brought about potential risk and security issues along with significant benefits. In order to enhance the security level of the 3D printing process, the present research aims to detect and recognize illegal components using deep learning. In this work, we collected a dataset of 61,340 2D images (28x28 for each image) of 10 classes including guns and other non-gun objects, corresponding to the projection results of the original 3D models. To validate the dataset, we train a convolutional neural network (CNN) model for gun classification which can achieve 98.16% classification accuracy.