YoloV8 Small Object Detection

[whittenator]

I am training a model using YOLOV8 with the need to detect small objects in IR imagery. Is there any tips/pointers that anyone in the community could give to enhance small object detection in IR imagery?

I am currently training a YoloV8-P2 small model but is there any other hyper-parameters or augmentations I can add to enhance detection?

The IR camera I plan to use this model on outputs images at a maximum resolution of 576x768(I know weird resolution), so the small output resolution makes it even more tough to detect small objects.

Thanks!

[glenn-jocher]

@whittenator detecting small objects, especially in IR imagery, can be challenging due to limited resolution and the nature of the objects' heat signatures. Here are a few tips to enhance small object detection with YOLOv8:

1. Model Choice: You're on the right track with YOLOv8-P2 for small objects. Its design caters to detecting smaller features.

2. Data Augmentation: Consider augmentations that emphasize small object features, such as random cropping, scaling, or adding noise specific to IR characteristics.

3. Input Resolution: Train with the highest resolution that your computational resources allow, ideally close to your camera's output resolution to preserve small object details.

4. Anchor Boxes: Customize anchor boxes to better match the size distribution of your small objects. YOLOv8 can auto-calculate these during training, but manual tuning might yield better results.

5. Loss Function: Experiment with different loss functions or adjustments that may give more weight to small object detection errors.

6. Batch Size and Learning Rate: A smaller batch size and an appropriate learning rate can sometimes help the model focus on finer details.

7. Transfer Learning: Use a pre-trained model on a similar domain or dataset to leverage learned features that might be relevant to your IR imagery.

8. Mosaic and MixUp: These augmentations can introduce more varied examples of small objects in different contexts and scales during training.

9. Focus on Relevant Features: Since you're dealing with IR imagery, ensure that the model is not overfitting to irrelevant features by using appropriate data normalization and preprocessing.

Remember, iterative experimentation is key. Monitor your validation metrics closely to understand the impact of your changes. For more detailed guidance on these modes and tasks, you can refer to the Ultralytics Docs. Good luck with your model training! 🚀