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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83817

    Title: 偵測與辨識後方來車的嵌入式深度學習系統;Rear-vehicle detection and recognition based on an embedded deep-learning system
    Authors: 賴秋燕;Lai, Chiu-Yen
    Contributors: 通訊工程學系在職專班
    Keywords: 先進駕駛輔助系統;樹莓派;神經運算棒;Advanced Driver Assistance Systems;Raspberry Pi;Neural Computer Stick 2
    Date: 2020-07-29
    Issue Date: 2020-09-02 17:10:05 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來各國政府為降低交通事故的發生率,針對車輛安全制定相關法規,在汽車系統中以先進駕駛輔助系統 (Advanced Driver Assistance Systems, ADAS) 輔助駕駛提高行車安全。在機車方面,國內政府是在民國86年制定,機車騎士與後座人員需強制配戴安全帽,否則可處罰鍰;另外在108年後出廠的機車需加裝安全設備:防鎖死煞車系統 (ABS) 或連動式煞車系統 (CBS) ,以提高機車行車安全。在行車安全意識抬頭下,許多機車會另外加裝行車記錄器,但這只是被動的記錄系統,無法主動提供即時安全偵測與警示功能。
    由於許多車禍原因來自於人為因素;例如,疲勞駕駛、路況不熟、超速、酒駕、後方來車追撞、大型車死角…等;因而本研究即針對後方來車提醒機車騎士需注意週遭車輛。行動載具必須具備輕量化的特性;因此,本研究的偵測與辨識系統使用嵌入式設備-樹莓派與NCS 2神經運算棒(Neural Computer Stick 2),加上輕量化的深度學習模型YOLOv3-Tiny並使用攝影機偵測機車後方的卡車、巴士、汽車、機車,在接近機車騎士前提醒機車騎士,為機車騎士添加一項主動安全設備,以達到即時偵測與辨識後方車輛的目的。
    在本論文中我們以在不增加網路深度的架構,及在第 5層與第6層加入Res block模塊來進行特徵提取運算,以960×540解析度的影片進行測試及比較,測試相較於原始的YOLOv3-Tiny 的執行速度有稍微降低由124 fps降為104 fps,但mAP從93.63%提升為96.71%。
    ;In recent years, in order to reduce the incidence of traffic accidents, governments of different countries have applied an Advanced Driver Assistance Systems (ADAS) to assist drivers and improve driving safety. In 1997, the Taiwan Government has set up a series of safety rules for motorcyclists, passengers and manufacturers. Motorcyclist and rear seat passenger must wear safety helmets when driving, or penalty will be charged. Motorcycles manufactured on or after 2019, must buddle with safety equipment, e.g. anti-lock braking system (ABS) or interlocking brake system (CBS) to improve driving safety. In the sake of safety, many motorcycles have additionally installed a driving recorder, however, which is a passive operation system and unable to provide an active real time detection and warning alert.
    Since many car accidents are caused by human factors, e.g. drowsy driving, unfamiliar road conditions, speeding, drunk driving, overtaking, blind corners etc.. Therefore, this research is aimed at alerting motorcyclists from vehicles coming from the rear and surroundings. To reduce the weight of the appliance, the detection and identification system uses embedded devices-Raspberry Pi and NCS 2 Neural Computer Stick 2 (Neural Computer Stick 2), plus a lightweight deep learning model YOLOv3-Tiny. In order to provide an active real time detection and safety driving environment, cameras are installed at the rear of vehicles to detect trucks, buses, cars, and motorcycles, warning will be given to motorcyclists once vehicles are approaching.
    Here we used an architecture that does not increase the depth of the network, by adding a Res block module to the 5th and 6th layers to perform feature extraction operations, and using a 960×540 resolution video to bring out the test and comparison. The execution speed of the traditional YOLOv3-Tiny is slightly reduced from 124 fps to 104 fps, mAP is increased from 93.63% to 96.71%.
    Appears in Collections:[通訊工程學系碩士在職專班 ] 博碩士論文

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