# Squeezed Edge YOLO: Onboard Object Detection on Edge Devices

Source: [Squeezed Edge YOLO: Onboard Object Detection on Edge Devices](<https://www.sota2.com/research/paper/68fa718e1e209131cea51f55>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:08:33.784Z

Authors: Edward Humes, Mozhgan Navardi, Tinoosh Mohsenin

Published: 2023-12-18T21:27:34.000Z

## Abstract

Demand for efficient onboard object detection is increasing due to its key role in autonomous navigation. However, deploying object detection models such as YOLO on resource constrained edge devices is challenging due to the high computational requirements of such models. In this paper, an compressed object detection model named Squeezed Edge YOLO is examined. This model is compressed and optimized to kilobytes of parameters in order to fit onboard such edge devices. To evaluate Squeezed Edge YOLO, two use cases - human and shape detection - are used to show the model accuracy and performance. Moreover, the model is deployed onboard a GAP8 processor with 8 RISC-V cores and an NVIDIA Jetson Nano with 4GB of memory. Experimental results show Squeezed Edge YOLO model size is optimized by a factor of 8x which leads to 76% improvements in energy efficiency and 3.3x faster throughout.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.11716>)

[PDF](<https://arxiv.org/pdf/2312.11716.pdf>)