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.
翻译:随着边缘计算的出现,越来越需要利用小形式要素边缘计算装置运行以进化神经网络为基础的物体探测,其计算和热预算有限,用于视频监视等应用。为解决这一问题,提出了诸如YOLO和SSD等高效的物体探测框架。然而,使用VGG16作为后端网络的SSD物体探测不足以在边缘设备上实现实时速度。为了进一步提高探测速度,后端网络被更高效的网络,如SqueezeNet和MoveNet等网络所取代。虽然速度大大改进,但价格却比较低。在本文件中,我们提出了名为Fire SSD的高效SSDSD 。消防SSD在Pascal VOC 2007 测试集上实现了70.7mAP。消防SSD在低功率主流CPU上达到30.6FPS的速度,比SSD300快6倍,而且其型号大约为4倍。FSDSDSDSDSDSD在综合GUP上也达到了22.2FS。