3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts
Date
2021-02-04Keywords
Computer vision
Deep learning
Transfer learning
Object recognition
Abstract
Deep learning methods have been successfully applied to image processing, mainly using
2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened
the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform
slightly worse than other 3D deep learning methods, and even worse than their 2D version. In
this paper, we propose to improve 3D deep learning results by transferring the pretrained weights
learned in 2D networks to their corresponding 3D version. Using an industrial object recognition
context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet,
Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is
obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable
results to state-of-the art methods. We also observed that the transfer approach enabled to improve
the accuracy of the Inception ResNet ...
Type
article