TrashDet: Iterative Neural Architecture Search for Efficient Waste Detection
Published:Dec 25, 2025 05:00
•1 min read
•ArXiv Vision
Analysis
This paper presents TrashDet, a novel framework for waste detection on edge and IoT devices. The iterative neural architecture search, focusing on TinyML constraints, is a significant contribution. The use of a Once-for-All-style ResDets supernet and evolutionary search alternating between backbone and neck/head optimization seems promising. The performance improvements over existing detectors, particularly in terms of accuracy and parameter efficiency, are noteworthy. The energy consumption and latency improvements on the MAX78002 microcontroller further highlight the practical applicability of TrashDet for resource-constrained environments. The paper's focus on a specific dataset (TACO) and microcontroller (MAX78002) might limit its generalizability, but the results are compelling within the defined scope.
Key Takeaways
- •TrashDet offers a novel approach to waste detection using iterative neural architecture search.
- •The framework is designed for TinyML constraints, making it suitable for edge and IoT devices.
- •Significant improvements in accuracy, parameter efficiency, energy consumption, and latency are demonstrated compared to existing methods.
Reference
“On a five-class TACO subset (paper, plastic, bottle, can, cigarette), the strongest variant, TrashDet-l, achieves 19.5 mAP50 with 30.5M parameters, improving accuracy by up to 3.6 mAP50 over prior detectors while using substantially fewer parameters.”