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With the emergence of edge computing, there is an increasing need for runningconvolutional neural network based object detection on small form factor edgecomputing devices with limited compute and thermal budget for applications suchas video surveillance. To address this problem, efficient object detectionframeworks such as YOLO and SSD were proposed. However, SSD based objectdetection that uses VGG16 as backend network is insufficient to achieve realtime speed on edge devices. To further improve the detection speed, the backendnetwork is replaced by more efficient networks such as SqueezeNet andMobileNet. Although the speed is greatly improved, it comes with a price oflower accuracy. In this paper, we propose an efficient SSD named Fire SSD. FireSSD achieves 70.7mAP on Pascal VOC 2007 test set. Fire SSD achieves the speedof 30.6FPS on low power mainstream CPU and is about 6 times faster than SSD300and has about 4 times smaller model size. Fire SSD also achieves 22.2FPS onintegrated GPU.

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