红外摄像机醉酒检测的深度学习技术

2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST) Pub Date : 2020-07-01 DOI:10.1109/ICEAST50382.2020.9165395

Pisit Iamudomchai, Pattanawadee Seelaso, Satjana Pattanasak, W. Piyawattanametha

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引用次数: 7

摘要

该项目旨在解决酒精测量延迟的问题，并减少呼吸分析仪测试产生的污染问题。本课题的重点是设计一种基于红外摄像机的深度学习酒精检测系统。本系统由两部分组成。第一部分是红外相机(FLIR)，用于采集红外和正常图像，接下来是基于深度学习技术的酒精检测图像处理系统，运行在iPhone操作系统(iOS)手机上。我们的手持式红外检测系统在4级分类(清醒、1杯、2杯或3杯)下的准确率为85.10%(135人)，在二元识别(清醒或醉酒)下的准确率为74.07%。每个杯子含有200毫升啤酒(5%的体积)。

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Deep Learning Technology for Drunks Detection with Infrared Camera

This project manipulates for the purpose to solve the problem of alcohol measurement delays and reduces the issue of contamination that arises from the breath analyzer test. This project focuses on designing a novel infrared (IR) camera-based alcohol detection system with deep learning technology. This system consists of 2 parts. The first part is an infrared camera (FLIR) used for collecting both IR and normal images then the next part is an image processing system for alcohol detection based on deep learning technology operating on an iPhone operating system (iOS) mobile phone. Our handheld IR based detection system achieves an accuracy of 85.10% (135 population) accuracy with 4 levels of classification (sober, 1 glass, 2 glasses, or 3 glasses) and 74.07% with binary identification (Sober or Drunk). Each glass contains 200 ml of beer (5% vol).

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2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST)

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