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imagerotationcaffedetection

How to mark detected objects in a new image?

I'm trying to detect multiple vehicles in satellite and aerial images. I have two main questions:

1- After training the convolution network and getting the caffe model, how could I test it on a new image and mark the detected vehicles with sth like bounding boxes? Should I change the size of data blob to be able to use commands like this?

net.forward('new image')

satellite image

2- As you know the vehicles on the streets have different angles. Are deep learning techniques already rotation invariant? If not what can I do to deal with object's angles which can vary to 360 degrees?

I would appreciate if anyone guide me through this.

Solution

  • You can use faster R-CNN base on caffe to train a vehicle detection model.

    1. Different image sizes can be input in the faster R-CNN framework, and there is demo code for you to reference.

    2. Because there are different angles vehicle in your training data, the trained model have the capacity to recognize these in new images.