# Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO

arXiv: https://arxiv.org/abs/2311.01057 · HTML: https://mlsanity.com/paper/2311.01057/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 85 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 30 of 48)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Fusion++: Volumetric Object-Level SLAM](https://mlsanity.com/paper/1808.08378/) (2018)
- [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://mlsanity.com/paper/2110.15352/) (2021)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression](https://mlsanity.com/paper/2003.08935/) (2020)

## Built on by (most cited first, 15 of 15)

- [ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data](https://mlsanity.com/paper/2402.01393/) (2024)
- [GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones](https://mlsanity.com/paper/2407.13706/) (2024)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [PicoSAM2: Low-Latency Segmentation In-Sensor for Edge Vision Applications](https://mlsanity.com/paper/2506.18807/) (2025)
- [DSORT-MCU: Detecting Small Objects in Real-Time on Microcontroller Units](https://mlsanity.com/paper/2410.16769/) (2024)
- [On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface](https://mlsanity.com/paper/2409.00083/) (2024)
- [TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection](https://mlsanity.com/paper/2603.16451/) (2026)
- [Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis](https://mlsanity.com/paper/2312.09854/) (2023)
- [Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow](https://mlsanity.com/paper/2408.02473/) (2024)
- [Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions](https://mlsanity.com/paper/2508.08352/) (2025)
- [PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation](https://mlsanity.com/paper/2603.11917/) (2026)
- [OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision](https://mlsanity.com/paper/2606.07431/) (2026)
- [MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes](https://mlsanity.com/paper/2605.15423/) (2026)
- [EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition](https://mlsanity.com/paper/2609.14498/) (2026)
- [You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition](https://mlsanity.com/paper/2609.16065/) (2026)

## Abstract (arXiv)

Smart glasses are rapidly gaining advanced functions thanks to cutting-edge computing technologies, especially accelerated hardware architectures, and tiny Artificial Intelligence (AI) algorithms. However, integrating AI into smart glasses featuring a small form factor and limited battery capacity remains challenging for a satisfactory user experience. To this end, this paper proposes the design of a smart glasses platform for always-on on-device object detection with an all-day battery lifetime. The proposed platform is based on GAP9, a novel multi-core RISC-V processor from Greenwaves Technologies. Additionally, a family of sub-million parameter TinyissimoYOLO networks are proposed. They are benchmarked on established datasets, capable of differentiating up to 80 classes on MS-COCO. Evaluations on the smart glasses prototype demonstrate TinyissimoYOLO's inference latency of only 17ms and consuming 1.59mJ energy per inference. An end-to-end latency of 56ms is achieved which is equivalent to 18 frames per seconds (FPS) with a total power consumption of 62.9mW. This ensures continuous system runtime of up to 9.3 hours on a 154mAh battery. These results outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image classification) at just 7.3 FPS, while the 18 FPS achieved in this paper even include image-capturing, network inference, and detection post-processing. The algorithm's code is released open with this paper and can be found here: https://github.com/ETH-PBL/TinyissimoYOLO