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

https://doi.org/10.3390/math10224299
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-49556-5_3openalex-snapshot
Early Experience in Benchmarking Edge AI Processors with Object Detection Workloads
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1016/j.eng.2020.01.007openalex-snapshot
A Survey of Accelerator Architectures for Deep Neural Networks
inferredcites →https://doi.org/10.1109/asap.2018.8445101openalex-snapshot
GAP-8: A RISC-V SoC for AI at the Edge of the IoT
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/edge55608.2022.00016openalex-snapshot
ECBA-MLI: Edge Computing Benchmark Architecture for Machine Learning Inference
inferredcites →https://doi.org/10.1109/ic2e52221.2021.00016openalex-snapshot
DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices
inferredcites →https://doi.org/10.1109/icecs46596.2019.8965067openalex-snapshot
PULP-NN: A Computing Library for Quantized Neural Network inference at the edge on RISC-V Based Parallel Ultra Low Power Clusters
inferredcites →https://doi.org/10.1109/jiot.2016.2579198openalex-snapshot
Edge Computing: Vision and Challenges
inferredcites →https://doi.org/10.1109/smartcloud.2016.18openalex-snapshot
Challenges and Opportunities in Edge Computing
inferredcites →https://doi.org/10.1145/1365490.1365500openalex-snapshot
Scalable Parallel Programming with CUDA
inferredcites →https://doi.org/10.1145/3079856.3080246openalex-snapshot
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3363347.3363363openalex-snapshot
Resource Characterisation of Personal-Scale Sensing Models on Edge Accelerators
inferredcites →https://doi.org/10.1145/3444692openalex-snapshot
A Survey on Edge Performance Benchmarking
inferredcites →https://doi.org/10.1587/transinf.2020edp7160openalex-snapshot
Benchmarking Modern Edge Devices for AI Applications
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.3390/cryptography6020016openalex-snapshot
Benchmark Analysis of YOLO Performance on Edge Intelligence Devices

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cpe.70118openalex-snapshot
Secuyolo: Secure Object Detection Framework With Darknet‐53 and CBAM in Edge Devices
inferred← citeshttps://doi.org/10.1007/978-3-031-52760-9_11openalex-snapshot
Open-Source Visual Target-Tracking System Both on Simulation Environment and Real Unmanned Aerial Vehicles
inferred← citeshttps://doi.org/10.1007/978-3-031-74440-2_4openalex-snapshot
Negative Affective State Vocalization Analysis of Dairy Cattle Using 3D MFCC Features with CNN-LSTM Model on an Edge Device
inferred← citeshttps://doi.org/10.1007/978-981-96-0805-8_11openalex-snapshot
Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices
inferred← citeshttps://doi.org/10.1007/s11227-024-06605-9openalex-snapshot
Balanced segmentation of CNNs for multi-TPU inference
inferred← citeshttps://doi.org/10.1007/s11554-024-01482-0openalex-snapshot
A power-aware vision-based virtual sensor for real-time edge computing
inferred← citeshttps://doi.org/10.1080/17483107.2025.2590679openalex-snapshot
Re-imagining sensory substitution through gestural control: Point-To-Tell 2
inferred← citeshttps://doi.org/10.1109/access.2023.3347548openalex-snapshot
Near-Edge Computing Aware Object Detection: A Review
inferred← citeshttps://doi.org/10.1109/icarm65671.2025.11293573openalex-snapshot
Development of a Waste Collection Robot for Train Carriages Interior
inferred← citeshttps://doi.org/10.1109/isitia66279.2025.11137459openalex-snapshot
Performance Comparison of Edge Devices for Deep Learning-Based Object Detection and Classification
inferred← citeshttps://doi.org/10.1109/pdp59025.2023.00020openalex-snapshot
Improving inference time in multi-TPU systems with profiled model segmentation
inferred← citeshttps://doi.org/10.1109/percomworkshops59983.2024.10503463openalex-snapshot
A Vision-based Virtual Sensor to Enhance Privacy and Energy Efficiency on Edge Computing
inferred← citeshttps://doi.org/10.1109/robio58561.2023.10354769openalex-snapshot
Design of an Automated Robotic System for Object Tracking and Targeted Lighting
inferred← citeshttps://doi.org/10.1109/tim.2025.3574911openalex-snapshot
Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare
inferred← citeshttps://doi.org/10.1142/s0218126626502270openalex-snapshot
StockBot: An Autonomous Smart Shelf Management and Restock Robot
inferred← citeshttps://doi.org/10.1145/3744339openalex-snapshot
Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
inferred← citeshttps://doi.org/10.1155/er/2245366openalex-snapshot
An Efficient Algorithm for Demand‐Response Optimization in Smart Grid Power Communication Based on Edge‐IoT
inferred← citeshttps://doi.org/10.24191/jsst.v3i2.35openalex-snapshot
Design of Automatic Truck Axle Counter Using Deep Learning on NVIDIA Jetson Nano
inferred← citeshttps://doi.org/10.3390/agronomy16030387openalex-snapshot
Research on an Automatic Seeding Performance Detection and Intelligent Reseeding Device for Leafy Vegetable Plug Seedlings
inferred← citeshttps://doi.org/10.3390/app13169222openalex-snapshot
Inference Latency Prediction Approaches Using Statistical Information for Object Detection in Edge Computing
inferred← citeshttps://doi.org/10.3390/electronics15040810openalex-snapshot
An FPGA-Based YOLOv5n Accelerator for Online Multi-Track Particle Localization
inferred← citeshttps://doi.org/10.3390/fi17090417openalex-snapshot
Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications
inferred← citeshttps://doi.org/10.3390/s23052802openalex-snapshot
State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear
inferred← citeshttps://doi.org/10.3390/s23073691openalex-snapshot
POSEIDON: A Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments
inferred← citeshttps://doi.org/10.3390/s24072046openalex-snapshot
Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation
inferred← citeshttps://doi.org/10.3390/s25185681openalex-snapshot
A Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators
inferred← citeshttps://doi.org/10.4018/979-8-2600-0701-3.ch001openalex-snapshot
Edge AI-Driven Latency Optimization in Real-Time Robotic Control
inferred← citeshttps://doi.org/10.4018/979-8-3373-7340-9.ch003openalex-snapshot
Bringing Generative AI to Edge Devices
inferred← citeshttps://doi.org/10.67294/97s55377openalex-snapshot
Intelligent Surveillance System for Knife Detection Based on YOLOv8 Neural Networks
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