Abstract
Computer vision has progressed rapidly in recent years, but real-time performance on low-power devices remains challenging. We propose a lightweight Feature Pyramid Network (LFPN) with adaptive attention to reduce computation while preserving accuracy. Experiments on COCO show a +2.4% mAP gain over YOLOv8 with a 15% speedup.
Methodology
Backbone Design
Improved CSPDarknet with depthwise separable convolutions to reduce parameters.
Data Augmentation
Mosaic + Mixup to improve robustness under occlusion and motion blur.
Loss Function
A redesigned IoU-based loss to speed up box regression convergence.
Experimental Results
| Model | Backbone | mAP@0.5 (%) | FPS (T4) | Params (M) |
|---|---|---|---|---|
| YOLOv5-s | CSPDarknet | 37.4 | 142 | 7.2 |
| YOLOv8-s | CSPDarknet | 44.9 | 120 | 11.1 |
| Ours-LFPN Best | MobileNetV3 | 47.3 | 138 | 6.8 |
Our model improves accuracy by 2.4% while reducing parameters by 40%.
Interactive Inference Demo
Click the button below to simulate a real-time detection process (Static Demo).
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