Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

TL;DR

Fire SSD introduces a novel, efficient object detection model optimized for edge devices, achieving high speed and accuracy with smaller model size using Fire modules.

Contribution

The paper proposes Fire SSD, a new architecture that significantly improves detection speed and reduces model size while maintaining high accuracy on edge devices.

Findings

Achieves 70.7 mAP on Pascal VOC 2007

Runs at 30.6 FPS on CPU

6 times faster than SSD300

Abstract

With the emergence of edge computing, there is an increasing need for running convolutional neural network based object detection on small form factor edge computing devices with limited compute and thermal budget for applications such as video surveillance. To address this problem, efficient object detection frameworks such as YOLO and SSD were proposed. However, SSD based object detection that uses VGG16 as backend network is insufficient to achieve real time speed on edge devices. To further improve the detection speed, the backend network is replaced by more efficient networks such as SqueezeNet and MobileNet. Although the speed is greatly improved, it comes with a price of lower accuracy. In this paper, we propose an efficient SSD named Fire SSD. Fire SSD achieves 70.7mAP on Pascal VOC 2007 test set. Fire SSD achieves the speed of 30.6FPS on low power mainstream CPU and is about 6 times faster than SSD300 and has about 4 times smaller model size. Fire SSD also achieves 22.2FPS on integrated GPU.