Abstract
This paper addresses trash detection on the TACO dataset under strict TinyML constraints using an iterative hardware-aware neural architecture search framework targeting edge and IoT devices. The proposed method constructs a Once-for-All-style ResDets supernet and performs iterative evolutionary search that alternates between backbone and neck/head optimization, supported by a population passthrough mechanism and an accuracy predictor to reduce search cost and improve stability. This framework yields a family of deployment-ready detectors, termed TrashDets. 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. The TrashDet family spans 1.2M to 30.5M parameters with mAP50 values between 11.4 and 19.5, providing scalable detector options for diverse TinyML deployment budgets on resource-constrained hardware. On the MAX78002 microcontroller with the TrashNet dataset, two specialized variants, TrashDet-ResNet and TrashDet-MBNet, jointly dominate the ai87-fpndetector baseline, with TrashDet-ResNet achieving 7525~$\mu$J energy per inference at 26.7 ms latency and 37.45 FPS, and TrashDet-MBNet improving mAP50 by 10.2%; together they reduce energy consumption by up to 88%, latency by up to 78%, and average power by up to 53% compared to existing TinyML detectors.
Abstract (translated)
这篇论文在严格的小型机器学习（TinyML）约束条件下，使用迭代硬件感知神经架构搜索框架，在TACO数据集上进行垃圾检测研究，该框架面向边缘计算和物联网设备。提出的方法构建了一个Once-for-All风格的ResDets超级网络，并通过交替优化骨干网和颈部/头部部分来进行迭代进化搜索，同时利用种群传递机制和准确度预测器来减少搜索成本并提高稳定性。这种框架生成了一组部署就绪的检测器，称为TrashDets。 在TACO的一个五类子集（纸、塑料、瓶子、罐头、烟蒂）上，最强版本TrashDet-l实现了30.5M参数和19.5 mAP50的性能，在使用明显更少的参数的同时，比之前的检测器提高了高达3.6 mAP50的准确性。TrashDets系列涵盖了从1.2M到30.5M不等的参数量，并且mAP50值在11.4和19.5之间变化，为资源受限硬件上的多样TinyML部署预算提供了可扩展的选择。 在配备了TrashNet数据集的MAX78002微控制器上，两种专门设计的变体——TrashDet-ResNet和TrashDet-MBNet——共同超越了ai87-fpndetector基准。其中，TrashDet-ResNet实现了每推理7525 μJ的能量消耗，在26.7毫秒延迟和37.45 FPS速度下工作；而TrashDet-MBNet将mAP50提高了10.2%。这两者相比现有的TinyML检测器，最多可以减少高达88%的能耗、78%的延迟，并且平均功率最多降低53%。
URL
https://arxiv.org/abs/2512.20746
