现在的CNN（卷积神经网络）作为DetectNet旋转不变吗？

问题描述

众所周知，用于对象检测的nVidia DetectNet - CNN（卷积神经网络）基于来自Yolo / DenseBox的方法：

现在的CNN（卷积神经网络）作为DetectNet旋转不变吗？

我可以训练DetectNet在成千上万个不同的图像与一个相同的旋转角度的对象，以检测任何旋转角度的对象？

旋转不变的：Yolo，Yolo v2，DenseBox基于哪个DetectNet？

解决方案

否

CNN不旋转不变。

您可以训练CNN将图像分类到预定义的类别中（如果要检测多个对象

在您的示例中，您需要使用分类器扫描图像的每个地方。

CNN对于训练数据中的小水平或垂直运动是不变的。 / p>

As known nVidia DetectNet - CNN (convolutional neural network) for object detection is based on approach from Yolo/DenseBox: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

And as shown here, DetectNet can detects objects (cars) with any rotations: https://devblogs.nvidia.com/parallelforall/detectnet-deep-neural-network-object-detection-digits/

Are modern CNN (convolutional neural network) as DetectNet rotate invariant?

Can I train DetectNet on thousands different images with one the same rotation angle of object, to detect objects on any rotation angles?

And what about rotate invariant of: Yolo, Yolo v2, DenseBox on which based DetectNet?

解决方案

No

CNNs are not rotate invariant. You need to include in your training set images with every possible rotation.

You can train a CNN to classify images into predefined categories (if you want to detect several objects in a image as in your example you need to scan every place of a image with your classifier).

A CNN is invariant to small horizontal or vertical movements in your training data.

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