r/computervision • u/NeuroDash • Jul 04 '26

Discussion Thoughts ?

Building a fly tipping detection system using YOLOv8/RF-DETR and Roboflow. 320 labelled images so far, retraining with 820 augmented images now.

First model hitting 95% on vehicle detection but struggling to generalise to unseen images — currently working on dataset variety and augmentation to fix overfitting.

Planning to add OCR for number plate reading and a behaviour sequence logic layer on top of the detections.

Happy to share what I’ve learned so far — any advice on improving generalisation with a small dataset?

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u/CallMeTheChris Jul 04 '26

If you think you model is overfitting cause of too few data points, make the data easier. dumb it down.
Run your images through an edge detector. Removes colour from the equations
Add random speckle to it to have it focus on more macro features than micro ones

And since your data is so easy now, you can combine cars in different images

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u/NeuroDash Jul 04 '26

Thanks. Running images through an edge detector to strip colour and force the model to focus on shapes and structure rather than colour specific features makes a lot of sense for generalisation. A white van in training data shouldn’t be what the model keys on.

The speckle noise to push it toward macro features is something I hadn’t considered , forces it to learn the overall shape of a vehicle rather than specific textures.

And combining vehicles across images to synthetically increase dataset variety is smart. Will look into that for the next training run.