Academic Journal
Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
| Title: | Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions |
|---|---|
| Authors: | El Zeinaty, Christophe, Hamidouche, Wassim, Herrou, Glenn, Menard, Daniel |
| Contributors: | allard, christelle |
| Source: | ACM Computing Surveys. 58:1-48 |
| Publication Status: | Preprint |
| Publisher Information: | Association for Computing Machinery (ACM), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, TinyML, Artificial intelligence, Object detection, Computer Vision and Pattern Recognition (cs.CV), Computing methodologies, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], edge computing, Model compression, Computer vision problems, embedded systems, Computer vision, Computer Vision and Pattern Recognition |
| Description: | Object detection (OD) has become vital for numerous computer vision applications, but deploying it on resource-constrained internet of things (IoT) devices presents a significant challenge. These devices, often powered by energy-efficient microcontrollers, struggle to handle the computational load of deep learning-based object detection (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 OD at the edge. Although numerous survey articles 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 article 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 (AI) 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: Link. |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1557-7341 0360-0300 |
| DOI: | 10.1145/3744339 |
| DOI: | 10.48550/arxiv.2508.08352 |
| Access URL: | http://arxiv.org/abs/2508.08352 https://hal.science/hal-05387002v1 https://hal.science/hal-05387002v1/document https://doi.org/10.1145/3744339 |
| Rights: | arXiv Non-Exclusive Distribution CC BY NC |
| Accession Number: | edsair.doi.dedup.....a9bef87197d6e40573835fa0b58bf676 |
| Database: | OpenAIRE |
| ISSN: | 15577341 03600300 |
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| DOI: | 10.1145/3744339 |