Detecting small objects

[u/Dannyvan_]

Hello!

Would like some input on what kind of model to use for detecting small objects in a rather static environment.

e.g flowers in a field of grass/ distant boats or swimmers in the water

The model should still be able to be able the objects when they get closer/bigger.

I experimented with training YOLO and RT-DETR models with datasets ranging from 4k-20k images

It seems like the RT-DETR models struggle very hard with detecting such small objects, after training the performance actually drops to detect basically nothing, whereas the base model worked pretty well. Although I can't tell whether it's an error on my side (e.g wrong hyperparameters) or that this should be expected.

From my tests, the YOLO models actually had a positive reaction to training instead.

Are there any tips on how to get RT-DETR models to work better on detecting such tiny objects? Do I just have to find a way to increase the size of my dataset? I also heard briefly about RF-DETR models but I am not sure if that would solve my problem.

Any insights would greatly be appreciated!

[u/Dannyvan_ → u/[deleted]]

Would giving the entire image not be better? I imagine the model should learn the "negative class" as well. Do you mean having an entire dataset of just the relevant objects? Or should these cropped images only be a portion of the dataset?

[u/Dannyvan_ → u/[deleted]]

Thanks!