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Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-22796-8_7openalex-snapshot

A Two-Teacher Framework for Knowledge Distillation

inferredcites →https://doi.org/10.1007/978-3-031-19803-8_5openalex-snapshot

TinyViT: Fast Pretraining Distillation for Small Vision Transformers

inferredcites →https://doi.org/10.1007/978-3-031-20044-1_38openalex-snapshot

EAutoDet: Efficient Architecture Search for Object Detection

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_25openalex-snapshot

Large-Scale R-CNN with Classifier Adaptive Quantization

inferredcites →https://doi.org/10.1007/s10462-024-10877-1openalex-snapshot

A comprehensive survey of deep learning-based lightweight object detection models for edge devices

inferredcites →https://doi.org/10.1007/s11227-020-03600-8openalex-snapshot

A systematic study of load balancing approaches in the fog computing environment

inferredcites →https://doi.org/10.1007/s11265-017-1255-5openalex-snapshot

Efficient Object Detection Using Embedded Binarized Neural Networks

inferredcites →https://doi.org/10.1007/s11554-023-01379-4openalex-snapshot

Lite-YOLOv3: a real-time object detector based on multi-scale slice depthwise convolution and lightweight attention mechanism

inferredcites →https://doi.org/10.1016/j.engappai.2024.109703openalex-snapshot

Enhancing camouflaged object detection through contrastive learning and data augmentation techniques

inferredcites →https://doi.org/10.1016/j.eswa.2022.119348openalex-snapshot

MobileDenseNet: A new approach to object detection on mobile devices

inferredcites →https://doi.org/10.1016/j.patcog.2020.107571openalex-snapshot

CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection

inferredcites →https://doi.org/10.1016/j.patrec.2019.03.026openalex-snapshot

BDNN: Binary convolution neural networks for fast object detection

inferredcites →https://doi.org/10.1016/j.patrec.2021.12.012openalex-snapshot

Data-adaptive binary neural networks for efficient object detection and recognition

inferredcites →https://doi.org/10.1016/j.vlsi.2019.07.005openalex-snapshot

Computer vision algorithms and hardware implementations: A survey

inferredcites →https://doi.org/10.1023/a:1023052124951openalex-snapshot

Contextual Priming for Object Detection

inferredcites →https://doi.org/10.1038/s41586-024-07976-yopenalex-snapshot

Bendable non-silicon RISC-V microprocessor

inferredcites →https://doi.org/10.1049/trit.2018.1026openalex-snapshot

Fast object detection based on binary deep convolution neural networks

inferredcites →https://doi.org/10.1109/access.2023.3347548openalex-snapshot

Near-Edge Computing Aware Object Detection: A Review

inferredcites →https://doi.org/10.1109/access.2024.3404878openalex-snapshot

Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems

inferredcites →https://doi.org/10.1109/asp-dac52403.2022.9712530openalex-snapshot

SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning

inferredcites →https://doi.org/10.1109/cvpr.2001.990517openalex-snapshot

Rapid object detection using a boosted cascade of simple features

inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/cvpr.2015.7298958openalex-snapshot

Recurrent convolutional neural network for object recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.776openalex-snapshot

Mimicking Very Efficient Network for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot

MobileNetV2: Inverted Residuals and Linear Bottlenecks

inferredcites →https://doi.org/10.1109/cvpr.2018.00913openalex-snapshot

Path Aggregation Network for Instance Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2019.00292openalex-snapshot

Fully Quantized Network for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2019.00720openalex-snapshot

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2019.01152openalex-snapshot

Importance Estimation for Neural Network Pruning

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00212openalex-snapshot

BiDet: An Efficient Binarized Object Detector

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01433openalex-snapshot

Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01563openalex-snapshot

CrossKD: Cross-Head Knowledge Distillation for Object Detection

inferredcites →https://doi.org/10.1109/cvpr52733.2024.01599openalex-snapshot

YOLO-World: Real-Time Open-Vocabulary Object Detection

inferredcites →https://doi.org/10.1109/cvprw.2019.00084openalex-snapshot

The Power of Tiling for Small Object Detection

inferredcites →https://doi.org/10.1109/cvprw63382.2024.00802openalex-snapshot

Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity

inferredcites →https://doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00032openalex-snapshot

Knowledge Distillation for Real-time On-Road Risk Detection

inferredcites →https://doi.org/10.1109/edge62653.2024.00023openalex-snapshot

Characterizing Deep Learning Model Compression with Post-Training Quantization on Accelerated Edge Devices

inferredcites →https://doi.org/10.1109/etfa45728.2021.9613668openalex-snapshot

Edge devices object detection by filter pruning

inferredcites →https://doi.org/10.1109/icassp40776.2020.9054101openalex-snapshot

Lightdet: A Lightweight and Accurate Object Detection Network

inferredcites →https://doi.org/10.1109/icassp43922.2022.9746843openalex-snapshot

Depth Pruning with Auxiliary Networks for Tinyml

inferredcites →https://doi.org/10.1109/icassp48485.2024.10447958openalex-snapshot

Dicetrack: Lightweight Dice Classification on Resource-Constrained Platforms with Optimized Deep Learning Models

inferredcites →https://doi.org/10.1109/iccv.2005.129openalex-snapshot

Joint Haar-like features for face detection

inferredcites →https://doi.org/10.1109/iccv.2017.298openalex-snapshot

Learning Efficient Convolutional Networks through Network Slimming

inferredcites →https://doi.org/10.1109/iccv.2019.00667openalex-snapshot

CenterNet: Keypoint Triplets for Object Detection

inferredcites →https://doi.org/10.1109/iccv51070.2023.01556openalex-snapshot

XiNet: Efficient Neural Networks for tinyML

inferredcites →https://doi.org/10.1109/iccvw54120.2021.00091openalex-snapshot

Knowledge Distillation for Low-Power Object Detection: A Simple Technique and Its Extensions for Training Compact Models Using Unlabeled Data

inferredcites →https://doi.org/10.1109/icemi.2017.8265863openalex-snapshot

Human face detection algorithm via Haar cascade classifier combined with three additional classifiers

inferredcites →https://doi.org/10.1109/icip.2017.8297075openalex-snapshot

Low-light pedestrian detection from RGB images using multi-modal knowledge distillation

inferredcites →https://doi.org/10.1109/icoias53694.2021.00009openalex-snapshot

Research on Object Detection Network Based on Knowledge Distillation

inferredcites →https://doi.org/10.1109/icra57147.2024.10610325openalex-snapshot

Object-centric Cross-modal Feature Distillation for Event-based Object Detection

inferredcites →https://doi.org/10.1109/infocom52122.2024.10621387openalex-snapshot

DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning

inferredcites →https://doi.org/10.1109/ispacs48206.2019.8986357openalex-snapshot

AIoT Solution Survey and Comparison in Machine Learning on Low-cost Microcontroller

inferredcites →https://doi.org/10.1109/ispass57527.2023.00024openalex-snapshot

CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (TinyML) Acceleration on FPGAs

inferredcites →https://doi.org/10.1109/jproc.2023.3238524openalex-snapshot

Object Detection in 20 Years: A Survey

inferredcites →https://doi.org/10.1109/mcas.2023.3302182openalex-snapshot

Tiny Machine Learning: Progress and Futures [Feature]

inferredcites →https://doi.org/10.1109/mce.2022.3181759openalex-snapshot

Lightweight Deep Learning: An Overview

inferredcites →https://doi.org/10.1109/mssc.2020.3002140openalex-snapshot

How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful

inferredcites →https://doi.org/10.1109/sec.2016.38openalex-snapshot

DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition

inferredcites →https://doi.org/10.1109/sensors56945.2023.10325167openalex-snapshot

TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation

inferredcites →https://doi.org/10.1109/tip.2024.3501853openalex-snapshot

ESOD: Efficient Small Object Detection on High-Resolution Images

inferredcites →https://doi.org/10.1109/tits.2022.3158253openalex-snapshot

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

inferredcites →https://doi.org/10.1109/tnn.1994.8753425openalex-snapshot

Dynamic recurrent neural networks: Theory and applications

inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot

Object Detection with Discriminatively Trained Part-Based Models

inferredcites →https://doi.org/10.1109/tpami.2024.3369699openalex-snapshot

Vision-Language Models for Vision Tasks: A Survey

inferredcites →https://doi.org/10.1109/wacv57701.2024.00094openalex-snapshot

Efficient Feature Distillation for Zero-shot Annotation Object Detection

inferredcites →https://doi.org/10.1109/waspaa.2019.8937164openalex-snapshot

ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

inferredcites →https://doi.org/10.1145/2043643.2043647openalex-snapshot

SpiNNaker

inferredcites →https://doi.org/10.1145/3289602.3293904openalex-snapshot

REQ-YOLO

inferredcites →https://doi.org/10.1145/3508391openalex-snapshot

TensorRT-Based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards

inferredcites →https://doi.org/10.1145/3546192openalex-snapshot

Reaching for the Sky: Maximizing Deep Learning Inference Throughput on Edge Devices with AI Multi-Tenancy

inferredcites →https://doi.org/10.1145/3657282openalex-snapshot

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

inferredcites →https://doi.org/10.1609/aaai.v34i07.6787openalex-snapshot

Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

inferredcites →https://doi.org/10.1609/aaai.v36i4.20387openalex-snapshot

EtinyNet: Extremely Tiny Network for TinyML

inferredcites →https://doi.org/10.18653/v1/2022.emnlp-main.741openalex-snapshot

EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation

inferredcites →https://doi.org/10.23919/mipro55190.2022.9803406openalex-snapshot

YOLO-Based Face Mask Detection on Low-End Devices Using Pruning and Quantization

inferredcites →https://doi.org/10.23919/splitech58164.2023.10193138openalex-snapshot

Dataset distillation as an enabling technique for on-device training in TinyML for IoT: an RFID use case

inferredcites →https://doi.org/10.3390/electronics13081595openalex-snapshot

Shared Knowledge Distillation Network for Object Detection

inferredcites →https://doi.org/10.3390/electronics13132598openalex-snapshot

A Study on GAN-Based Car Body Part Defect Detection Process and Comparative Analysis of YOLO v7 and YOLO v8 Object Detection Performance

inferredcites →https://doi.org/10.3390/make5040083openalex-snapshot

A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

inferredcites →https://doi.org/10.3390/math10224299openalex-snapshot

An Evaluation of Modern Accelerator-Based Edge Devices for Object Detection Applications

inferredcites →https://doi.org/10.3390/s23031185openalex-snapshot

Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices

inferredcites →https://doi.org/10.48550/arxiv.1808.05377openalex-snapshot

Neural Architecture Search: A Survey

inferredcites →https://doi.org/10.48550/arxiv.2211.09788openalex-snapshot

DiffusionDet: Diffusion Model for Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2303.05015openalex-snapshot

Smooth and Stepwise Self-Distillation for Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2304.08069openalex-snapshot

DETRs Beat YOLOs on Real-time Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2304.10970openalex-snapshot

Can GPT-4 Perform Neural Architecture Search?

inferredcites →https://doi.org/10.48550/arxiv.2307.07662openalex-snapshot

MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression

inferredcites →https://doi.org/10.48550/arxiv.2405.00892openalex-snapshot

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications

inferredcites →https://doi.org/10.52202/079017-2031openalex-snapshot

ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models

inferredcites →https://doi.org/10.5281/zenodo.3908559openalex-snapshot

ultralytics/yolov5: Initial Release

inferredcites →https://doi.org/10.5281/zenodo.7777862openalex-snapshot

Deci-AI/super-gradients: 3.0.0.1

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-36868-3_18openalex-snapshot

Prompt-Guided Resource-Aware Neural Architecture Search for TinyML Object Detection Models

inferred← citeshttps://doi.org/10.1007/978-3-032-37441-7_56openalex-snapshot

Impact Analysis of Convolutional Neural Network Quantization on Low-Cost Microcontrollers

inferred← citeshttps://doi.org/10.1007/978-3-032-37490-5_7openalex-snapshot

If It’s Not Efficient, It’s Not Usable: Real-Time OOD Detection with Latent De-Biasing and High-Quality Negative Samples

inferred← citeshttps://doi.org/10.1007/s40964-025-01401-0openalex-snapshot

Real-time defect monitoring in material extrusion 3D printing using optimized YOLO models

inferred← citeshttps://doi.org/10.1016/j.seta.2026.105078openalex-snapshot

Self-powered intelligent sensing networks for next-generation super airports

inferred← citeshttps://doi.org/10.1038/s41598-025-27818-9openalex-snapshot

Deploying TinyML for energy-efficient object detection and communication in low-power edge AI systems

inferred← citeshttps://doi.org/10.1038/s41598-026-38359-0openalex-snapshot

Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation

inferred← citeshttps://doi.org/10.1109/icima68728.2026.11564677openalex-snapshot

Latency-Aware Hardware–Software Co-Design of an ARM–NPU Heterogeneous Edge Architecture for Real-Time Neural Speech Intelligence

inferred← citeshttps://doi.org/10.1109/jiot.2026.3685248openalex-snapshot

TinyML for Eddy Current Testing: A Review of Advances, Challenges, and Applications

inferred← citeshttps://doi.org/10.1145/3774906.3802788openalex-snapshot

Short Paper: Counting Parked Bicycles on the Edge - A TinyML Smart City Application

inferred← citeshttps://doi.org/10.1145/3841475openalex-snapshot

UVirtio: Enabling Ubiquitous Resource Sharing for RISC-V Industrial Edge Devices

inferred← citeshttps://doi.org/10.3390/computation14050112openalex-snapshot

Design and Evaluation of a Compact VGG-Inspired CNN for Keyword Spotting in Resource-Constrained TinyML Systems

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