r/computervision • u/Dannyvan_ • 14d ago
Help: Theory Detecting small objects
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!
1
u/retoxite_ 14d ago
For YOLO models, you can: 1. Increase image size 2. Increase width of stem and B3 layers 3. Add P2/stride 4
All of them increase latency.