Object detection training on large images

Question

Trying to detect number plates by using faster RCNN on the images 4096 by 8192 pixels.

Instead of resizing for training I have cropped some parts of the image and labeled the number plate and trained, This way it's working but It cannot detect on actual images but only on small images.

Please guide me what is the best way to achieve such a job. How should I feed the training, and how the configuration has to be on faster_rcnn_inception_v2_pets.config. Or if you think faster RCNN is not good for this kind of job please suggest a better way, I need over %80 accuracy at least.

I have searched a lot on google but couldn't find anyone working on above 8k images.

I am attaching a sample image below as well. https://ibb.co/NKWWd7q

I have tried to do annotated training on 4096 by 8192 pixels images over google cloud servers, it consumed over 250gb RAM on single batch size.

Kind Regards.

If I got you right your trained network can detect number plates if they are more prominent in an image, but not in a big image (with small plates) like the one added. Right? One way in which you could keep your trained network is to segment cars from the background. By automatically cropping out the car you can run your plate detection again. To detect cars you have multiple options. — Mr.Sh4nnon
But if you are using Yolo it will resize your training data during training process, image size doesn't matter anymore — gameon67
@gameon67 Even tensorflow resize during the training process if i am right. — Profstyle
@Mr.Sh4nnon is it possible to classify cars as well then tell tensorflow find a car first than look for number plate. Is this truly possible, that would be faster and more accurate i think. — Profstyle
@Mr.Sh4nnon Do you know any opensource example which can guide me for such job. — Profstyle

Mr.Sh4nnon Mr.Sh4nnon · Accepted Answer · 2019-04-30T08:27:25

Here my answer as collections of my comments:

It seems that your number plates might be too small compared to the rest of the image.

Try to first extract the car with e.g. YOLO, extract it and then run your number-plate detection network again. Keep in mind that you maybe have to adjust the bounding box (the size of the car extracted) to fit your networks input size.

An example to detect cars with YOLO can be found here.

Object detection training on large images

1 Answers