This work is licensed under a Creative Commons Attribution 4.0 International License,which permits unrestricted use,distribution,and reproduction in any medium,provided the original work is properly cited.A Real Time Vision-Based Smoking Detection Framework on Edge  

作　　者：Ruilong Chen Guangfu Zeng Ke Wang Lei Luo Zhiping Cai 

机构地区：[1]College of Computer,National University of Defense Technology,Changsha,410073,China [2]College of Computer,Guizhou University of Finance and Economics,Guiyang,550025,China

出　　处：《Journal on Internet of Things》2020年第2期55-64,共10页

摘　　要：Smoking is the main reason for fire disaster and pollution in petrol station,construction site and warehouse.Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios.With the developments of closed circuit television(CCTV)system,vision-based methods for object detection,mostly driven by deep learning techniques,were introduced recently.However,the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed.This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,high accuracy on micro-objects and low computing budget,i.e.,it could be deployed on the edge device such as NVIDIA JETSON TX2.We designed a new framework named RTVBS based on yolov3 and made a smoking dataset to train our model.We raised several methods to improve detection accuracy during the training step.The validation results show our model has excellent performance in smoking detection.

关 键 词：Smoking detection small object detection real time CNN image processing 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]