# Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster

arXiv: https://arxiv.org/abs/2112.13194 · HTML: https://mlsanity.com/paper/2112.13194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 91 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems](https://mlsanity.com/paper/2102.10191/) (2021)
- [Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)](https://mlsanity.com/paper/1810.06938/) (2018)
- [End-to-End Simulation of 5G mmWave Networks](https://mlsanity.com/paper/1705.02882/) (2017)
- [Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People](https://mlsanity.com/paper/1904.13037/) (2019)
- [Millimeter Wave Channel Modeling via Generative Neural Networks](https://mlsanity.com/paper/2008.11006/) (2020)

## Built on by (most cited first, 3 of 3)

- [Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision](https://mlsanity.com/paper/2208.08477/) (2022)
- [Robust Computer-Vision based Construction Site Detection for Assistive-Technology Applications](https://mlsanity.com/paper/2503.04139/) (2025)
- [Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells](https://mlsanity.com/paper/2608.09658/) (2026)

## Abstract (arXiv)

Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing to the cloud. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection within a powerful, new smart wearable called VIS4ION, for the Blind-and-Visually Impaired (BVI). The current VIS4ION system is an instrumented book-bag with high-resolution cameras, vision processing and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge cloud to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and realistic assessment of edge computing with mmWave connectivity in an application with both high bandwidth and low latency requirements.