Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
Object detection (OD) has become vital for numerous computer vision applications, but deploying it on resource-constrained IoT devices presents a significant challenge. These devices, often powered by energy-efficient microcontrollers, struggle to handle the computational load of deep learning-based OD models. This issue is compounded by the rapid proliferation of IoT devices, predicted to surpass 150 billion by 2030. TinyML offers a compelling solution by enabling OD on ultra-low-power devices, paving the way for efficient and real-time processing at the edge. Although numerous survey papers have been published on this topic, they often overlook the optimization challenges associated with deploying OD models in TinyML environments. To address this gap, this survey paper provides a detailed analysis of key optimization techniques for deploying OD models on resource-constrained devices. These techniques include quantization, pruning, knowledge distillation, and neural architecture search. Furthermore, we explore both theoretical approaches and practical implementations, bridging the gap between academic research and real-world edge artificial intelligence deployment. Finally, we compare the key performance indicators (KPIs) of existing OD implementations on microcontroller devices, highlighting the achieved maturity level of these solutions in terms of both prediction accuracy and efficiency. We also provide a public repository to continually track developments in this fast-evolving field: https://github.com/christophezei/Optimizing-Object-Detection-Models-for-TinyML-A-Comprehensive-Survey.
Code (0)
등록된 구현이 없습니다.
Tasks
Neural Architecture SearchKnowledge DistillationObject DetectionSimilar Papers 제목 키워드 기반
OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting
Voice-triggered interfaces rely on keyword spotting (KWS) models that must operate continuously under strict memory, latency, and energy constraints on microcontroller-class hardware. Designing such models therefore requ…
Keyword SpottingTinySV: Speaker Verification in TinyML with On-device Learning
TinyML is a novel area of machine learning that gained huge momentum in the last few years thanks to the ability to execute machine learning algorithms on tiny devices (such as Internet-of-Things or embedded systems). In…
Keyword SpottingSpeaker VerificationTinyML for Ubiquitous Edge AI
TinyML is a fast-growing multidisciplinary field at the intersection of machine learning, hardware, and software, that focuses on enabling deep learning algorithms on embedded (microcontroller powered) devices operating …
Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies
Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure. While traditional predictive models, grounded in s…
Weather ForecastingData Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexity of designing TinyML systems hampers th…
Neural Architecture SearchKeyword Spotting