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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts
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3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts

机译：3D卷积神经网络从佩带的2D卷积神经网络初始化用于工业部件的分类

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摘要

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 3D version up to 18% with respect to the score of the 3D approach alone.
机译：深度学习方法已成功应用于图像处理，主要使用2D视觉传感器。最近，深度相机和其他类似的3D传感器的兴起为新的感知技术开辟了该领域。然而，3D卷积神经网络比其他3D深度学习方法略差，甚至比他们的2D版本更糟糕。在本文中，我们建议通过将2D网络中学到的预制权重转移到它们相应的3D版本来提高3D深度学习结果。使用工业对象识别上下文，我们分析了3D卷积网络的不同组合（VGG16，Reset，Inception Reset和AbseralNet），比较了识别准确性。使用挤出的高精度获得最高精度，精度为0.9217，这使得最先进的方法提供了可比的结果。我们还观察到转移方法能够在单独的3D方法的分数方面提高成立Reset 3D版本的准确性高达18％。

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