Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n
Quick Answer
The study evaluates the impact of synthetic and derived images on YOLOv8n's performance for campus waste detection, finding no configuration surpassed the real-only model's mAP@0.5 of 0.691.
Quick Take
Various augmentation strategies yielded mixed results, with background replacement reducing performance significantly. The small test set limits strong class-specific conclusions.
Key Points
- Real dataset: 148 campus photos with 86 for training, 31 for validation, and 31 for testing.
- Synthetic images did not improve YOLOv8n beyond the real-only model's mAP@0.5 of 0.691.
- Background replacement led to a significant drop in performance, reducing mean mAP to 0.560.
- Hand-composite experiments showed no reliable effect on detection performance.
- Small test set size limits the ability to draw strong class-specific conclusions.
Paper Resources
Source Excerpt
Incorrect disposal can contaminate campus recycling streams, and a bin-mounted camera could provide feedback as an item is discarded. We evaluated whether synthetic and derived images improve a YOLOv8n detector for this view. The real dataset contained 148 campus photographs: 86 for training, 31 for validation, and 31 for testing. Twelve joint-training configurations varied the amount and source of added images. We repeated seven principal settings with four matched seeds and computed bootstrap
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