Can we do model ensemble on several cascade classifiers ?

edit

because the object that I want to detect has different aspect ratios

can you explain that a bit better, please ?

For example, I have an object in which the aspect ratio could be 1:2, 2:1, 1:1, 3:1, etc. I would like to detect as many as possible this object. My actual solution is to train separately 4 models with the aspect ratio I mentioned above, then merge the result of detection. What I would like to know is that could I train 4 models but using some technologies like "model ensemble" or "voting" to give me one model which could detect the four aspect ratios at same time?

Probably you should look at deep learning stuff for object detection. There are many tutorials on how to train on custom data with Tensorflow. Have a look at these documentations.

You can then look at this page in OpenCV: TensorFlow Object Detection API.

Thanks for your reply. I have thought about deep learning before using cascade classifier.

The reason that I didn't use deep learning is that I have a problem which is not relevant to any public dataset. I need to train my model from scratch and maybe modify the architecture to adapt to my problem. What I need now is using some "safe" methods to get not bad results in a short period.

And I have many objects in an image which means I have few images to pass into the NN so I can't train my NN from scratch (I know cut and merge will work but this will cause me more works to do).

Maybe I will try it later.

This is my limited experience with deep learning:

• I don't train a network from scratch but rather do a fine tuning training from a checkpoint. This means that you reuse weights from a well-known network (MobileNet-SSD, FasterRCNN, ...) and fine tune it for your data
• I did it successfully to detect one class
• Have a look at the TensorFlow object detection doc
• I have also put some useful information in another answer

Main advantages against a cascade of classifiers:

• no issue with the aspect ratio
• fast training if you have a GPU and you can easily check the validity of the training with Tensorboard (loss curves, detection performed on the evaluation set)
• even with no experience, you should be able to do a first training that will successfully detect your class
• the hard part is to setup your environment but it was the same with cascade of classifiers training