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Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform

arXiv 2208.11011 ↗First posted 23 Aug 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network topology to fit hardware constraints. In this paper, we adapt one of the most widely used architectures for mobile hardware platforms, MobileNetV2, and study the impact of changing its topology and applying post-training quantization. We discuss the impact of the adaptations and the deployment of the model on an embedded hardware platform for face detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 23 Aug 2022.

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20 of 30 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  6. 2014Going Deeper with Convolutionscited by 4,241
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  8. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  9. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  10. 2019Searching for MobileNetV3cited by 1,994
  11. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  12. 2016YOLO9000: Better, Faster, Strongercited by 1,431
8 more
  1. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  2. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
  3. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  4. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 521
  5. 2015Compressing Neural Networks with the Hashing Trickcited by 150
  6. 2016Finding Tiny Facescited by 72
  7. 2017Faceness-Net: Face Detection through Deep Facial Part Responsescited by 8
  8. 2019Neural Architecture Search for Deep Face Recognitioncited by 3

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  1. 2023On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision
  2. 2023Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines
  3. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
  4. 2021Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
  5. 2025GhostNetV3-Small: A Tailored Architecture and Comparative Study of Distillation Strategies for Tiny Images
  6. 2023Fully Quantized Always-on Face Detector Considering Mobile Image Sensors
  7. 2021HPTQ: Hardware-Friendly Post Training Quantization
  8. 2022A Closer Look at Hardware-Friendly Weight Quantization