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YOLOv8n waste detection model underperforms with synthetic data

By PulseAugur Editorial · [1 sources] ·

A study published on arXiv evaluated the effectiveness of synthetic and derived images in improving a YOLOv8n object detection model for campus waste detection. The research found that none of the tested augmentation configurations, including background replacement and isolated-object images, surpassed the performance of a model trained solely on real-world campus photographs. The real-only model achieved a mean average precision of 0.691, while augmentation strategies resulted in lower scores, with the full augmentation pool yielding 0.487. The study also explored the impact of hand-and-forearm composites but found no reliable effect. AI

IMPACT Suggests that for specific computer vision tasks like campus waste detection, real-world data may still outperform synthetic augmentation.

RANK_REASON Academic paper detailing an evaluation of a specific model's performance with synthetic data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YOLOv8n waste detection model underperforms with synthetic data

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Academic paper detailing an evaluation of a specific model's performance with synthetic data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ali Behbahani, Newsha Javanmardi, Shahriar Ahmed, Ling Chen, Phouvadeth Vathana ·

    Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n

    arXiv:2607.19535v1 Announce Type: new Abstract: 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 re…

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