EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors

TL;DR

EdgeNet is a hierarchical framework that enables high-accuracy object detection on resource-constrained devices by reducing data and optimizing processing, suitable for real-time applications like UAVs.

Contribution

The paper introduces a novel data reduction mechanism that maintains CNN accuracy while significantly improving processing speed on low-power edge devices.

Findings

Reduces processed data by 100x

Achieves under 4W power consumption

Improves processing speed over existing methods

Abstract

Visual intelligence at the edge is becoming a growing necessity for low latency applications and situations where real-time decision is vital. Object detection, the first step in visual data analytics, has enjoyed significant improvements in terms of state-of-the-art accuracy due to the emergence of Convolutional Neural Networks (CNNs) and Deep Learning. However, such complex paradigms intrude increasing computational demands and hence prevent their deployment on resource-constrained devices. In this work, we propose a hierarchical framework that enables to detect objects in high-resolution video frames, and maintain the accuracy of state-of-the-art CNN-based object detectors while outperforming existing works in terms of processing speed when targeting a low-power embedded processor using an intelligent data reduction mechanism. Moreover, a use-case for pedestrian detection from Unmanned-Areal-Vehicle (UAV) is presented showing the impact that the proposed approach has on sensitivity, average processing time and power consumption when is implemented on different platforms. Using the proposed selection process our framework manages to reduce the processed data by 100x leading to under 4W power consumption on different edge devices.