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基於YOLO演算法與時間序列影像之道路監控系統 = = Based on...

陳姿妤

基於YOLO演算法與時間序列影像之道路監控系統 = = Based on YOLO Algorithm and Time-slice Images for Road Monitoring System /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	基於YOLO演算法與時間序列影像之道路監控系統 =/ 陳姿妤.
Reminder of title:	Based on YOLO Algorithm and Time-slice Images for Road Monitoring System /
remainder title:	Based on YOLO Algorithm and Time-slice Images for Road Monitoring System.
Author:	陳姿妤
Published:	雲林縣 :國立虎尾科技大學 , : 民113.08.,
Description:	[9], 67面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 蔡文凱.
Subject:	Motorcycle Intrusion. -
Online resource:	電子資源

基於YOLO演算法與時間序列影像之道路監控系統 = = Based on YOLO Algorithm and Time-slice Images for Road Monitoring System /
陳姿妤

基於YOLO演算法與時間序列影像之道路監控系統 =Based on YOLO Algorithm and Time-slice Images for Road Monitoring System /Based on YOLO Algorithm and Time-slice Images for Road Monitoring System.陳姿妤. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.08. - [9], 67面 :圖, 表 ;30公分.

指導教授: 蔡文凱.

碩士論文--國立虎尾科技大學電機工程系碩士班.

含參考書目.

隨著城市化進程的加快和人口的增長，交通管理已成為城市管理中至關重要的一環。本論文旨在開發一套高精準的車輛計數系統，以協助交通監控部門實時監控高速公路車流量，了解平日與假日的車流狀況，以分析出高峰期與離峰期，讓交通監控部門透過本論文的方法研擬出最好的交通政策。此外機車誤闖高速公路是一個嚴重的安全問題，本論文提出了針對機車誤闖的快速回報系統，以減少此類事件的發生。本論文的演算法流程分為四個階段：第一階段是預處理，利用YOLOv7進行車輛偵測並重新訓練模型以劃分車輛類型，以及設立感興趣區域；第二階段是時間序列影像堆疊，透過TSI技術提出time-slice-advance演算法以提升偵測速度與準確度；第三階段是車輛計數，使用掃描線搭配有限狀態機確保車輛計數的準確度；最後是機車警示，採用YOLOv7偵測機車，當偵測到機車會回傳訊息，以降低國道事故的發生。實驗結果證明，本論文所提出的方法透過11個影像數據集顯示在精確計數具有顯著優勢，在以車輛朝攝影機駛來的正向資料集中Precision可以達到99.7%，Recall可以達到98.9%；在以車輛駛離攝影機的反向資料集中Precision可以達到100%，Recall可以達到99.3%，以呈現出本論文所提出的演算法有著高精準的車輛計數。此外本論文與其他演算法進行相比，也獲得了較高的準確率。機車警示可利用YOLOv7偵測準確的偵測到機車，並提出警示，以減少憾事的發生。.

(平裝)Subjects--Topical Terms:

1451918
Motorcycle Intrusion.

基於YOLO演算法與時間序列影像之道路監控系統 = = Based on YOLO Algorithm and Time-slice Images for Road Monitoring System /
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245 1 0 $a 基於YOLO演算法與時間序列影像之道路監控系統 = $b Based on YOLO Algorithm and Time-slice Images for Road Monitoring System / $c 陳姿妤.
246 1 1 $a Based on YOLO Algorithm and Time-slice Images for Road Monitoring System.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.08.
300 $a [9], 67面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 蔡文凱.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學電機工程系碩士班.
504 $a 含參考書目.
520 3 $a 隨著城市化進程的加快和人口的增長，交通管理已成為城市管理中至關重要的一環。本論文旨在開發一套高精準的車輛計數系統，以協助交通監控部門實時監控高速公路車流量，了解平日與假日的車流狀況，以分析出高峰期與離峰期，讓交通監控部門透過本論文的方法研擬出最好的交通政策。此外機車誤闖高速公路是一個嚴重的安全問題，本論文提出了針對機車誤闖的快速回報系統，以減少此類事件的發生。本論文的演算法流程分為四個階段：第一階段是預處理，利用YOLOv7進行車輛偵測並重新訓練模型以劃分車輛類型，以及設立感興趣區域；第二階段是時間序列影像堆疊，透過TSI技術提出time-slice-advance演算法以提升偵測速度與準確度；第三階段是車輛計數，使用掃描線搭配有限狀態機確保車輛計數的準確度；最後是機車警示，採用YOLOv7偵測機車，當偵測到機車會回傳訊息，以降低國道事故的發生。實驗結果證明，本論文所提出的方法透過11個影像數據集顯示在精確計數具有顯著優勢，在以車輛朝攝影機駛來的正向資料集中Precision可以達到99.7%，Recall可以達到98.9%；在以車輛駛離攝影機的反向資料集中Precision可以達到100%，Recall可以達到99.3%，以呈現出本論文所提出的演算法有著高精準的車輛計數。此外本論文與其他演算法進行相比，也獲得了較高的準確率。機車警示可利用YOLOv7偵測準確的偵測到機車，並提出警示，以減少憾事的發生。.
520 3 $a With the acceleration of urbanization and population growth, traffic management has become a crucial aspect of urban administration. This paper aims to develop a highly accurate vehicle counting system to assist traffic monitoring departments in real-time monitoring of highway traffic flow, understanding the differences in traffic patterns between weekdays and holidays, and analyzing peak and off-peak periods. This will help the traffic monitoring department formulate the best traffic policies using the methods proposed in this paper. Additionally, motorcycle intrusions on highways pose a serious safety issue. This paper proposes a quick reporting system for motorcycle intrusions to reduce such incidents. The algorithm process in this paper is divided into four stages: the first stage is preprocessing, utilizing YOLOv7 for vehicle detection and retraining the model to classify vehicle types, and establishing regions of interest; the second stage is time-series image stacking, proposing the time-slice-advance algorithm through TSI technology to improve detection speed and accuracy; the third stage is vehicle counting, using scan lines combined with a finite state machine to ensure counting accuracy; and finally, the fourth stage is motorcycle warning, using YOLOv7 to detect motorcycles, sending alerts when motorcycles are detected to reduce highway accidents. Experimental results demonstrate that the proposed method exhibits significant advantages in accurate counting through 11 image datasets. In forward datasets where vehicles approach the camera, Precision can reach 99.7%, and Recall can reach 98.9%; in reverse datasets where vehicles move away from the camera, Precision can reach 100%, and Recall can reach 99.3%, showcasing the high accuracy of the proposed vehicle counting algorithm. Moreover, this paper's algorithm achieved higher accuracy compared to other algorithms. The motorcycle warning system accurately detects motorcycles using YOLOv7 and issues warnings, thereby reducing the occurrence of accidents..
563 $a (平裝)
650 # 4 $a Motorcycle Intrusion. $3 1451918
650 # 4 $a time-slice-advance Algorithm. $3 1451917
650 # 4 $a Vehicle Counting. $3 1451916
650 # 4 $a Vehicle Detection. $3 1451915
650 # 4 $a YOLOv7. $3 1416661
650 # 4 $a 機車誤闖. $3 1451914
650 # 4 $a time-slice-advance演算法. $3 1451913
650 # 4 $a 車輛計數. $3 1451912
650 # 4 $a 車輛偵測. $3 1451911
856 7 # $u https://handle.ncl.edu.tw/11296/7a27mc $z 電子資源 $2 http