Squeezed Edge YOLO: Onboard Object Detection on Edge Devices

TL;DR

This paper introduces Squeezed Edge YOLO, a highly compressed object detection model optimized for onboard edge devices, demonstrating significant improvements in size, energy efficiency, and speed for autonomous navigation applications.

Contribution

The paper presents a novel compressed version of YOLO, called Squeezed Edge YOLO, optimized for resource-constrained edge devices, with extensive evaluation on real hardware.

Findings

Model size reduced by 8x

Energy efficiency improved by 76%

Processing throughput increased by 3.3x

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.