Multi-Image Object Detection — Reference-Based Comparison
Traditional detection models analyze each image in isolation. ONE AI's overlap difference approach compares a test image against a reference, letting the model focus only on what changed — dramatically improving accuracy for surveillance and quality control.
Synthetic bird & drone detection dataset — small objects (7–10 % of image), complex city backgrounds, varying lighting. 259 image pairs.
Results
| Model | F1-Score | Model Size |
|---|---|---|
| ONE AI (overlap difference) | 93.2 % | Optimized, lightweight |
| YOLOv8 (single image) | 56.0 % | Pre-trained, heavy |
Key Findings
10× Fewer Errors
ONE AI achieves 93.2 % F1 vs. YOLOv8's 56 % — making more than 10× fewer errors on the same dataset.
Why the Gap Is So Large
By computing pixel-wise differences between reference and test images, ONE AI cancels out complex backgrounds automatically. YOLOv8 must learn to separate tiny objects from busy cityscapes using a single frame — a fundamentally harder problem.
8× Smaller Model
ONE AI's architecture search produces a model 8× smaller than YOLOv8, making it ideal for edge deployment across multiple inspection stations.
Takeaway
When spatially aligned reference images are available, multi-image comparison combined with automated architecture search delivers accuracy that single-image detectors cannot match — at a fraction of the model size.
Need Help? We're Here for You!
Christopher from our development team is ready to help with any questions about ONE AI usage, troubleshooting, or optimization. Don't hesitate to reach out!