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基於深度學習對越南交通流量檢測與違規行為分析 = = Traffic f...

潘秋香

基於深度學習對越南交通流量檢測與違規行為分析 = = Traffic flow detection and violation analysis in Vietnam based on deep learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	基於深度學習對越南交通流量檢測與違規行為分析 =/ 潘秋香.
Reminder of title:	Traffic flow detection and violation analysis in Vietnam based on deep learning /
remainder title:	Traffic flow detection and violation analysis in Vietnam based on deep learning.
Author:	潘秋香
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[12], 114面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 林彥昆.
Subject:	Paddle OCR. -
Online resource:	電子資源

基於深度學習對越南交通流量檢測與違規行為分析 = = Traffic flow detection and violation analysis in Vietnam based on deep learning /
潘秋香

基於深度學習對越南交通流量檢測與違規行為分析 =Traffic flow detection and violation analysis in Vietnam based on deep learning /Traffic flow detection and violation analysis in Vietnam based on deep learning.潘秋香. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [12], 114面 :圖, 表 ;30公分.

指導教授: 林彥昆.

碩士論文--國立虎尾科技大學資訊管理系碩士班.

含參考書目.

智慧城市提高了城市地區的效率、永續性和宜居性，從而促進經濟成長並提高公民的生活水平。這使得智慧城市對於國家的發展至關重要。在全球智慧城市領域，整合智慧交通技術對於改善城市和確保高效的交通管理至關重要。動態交通流和管理系統是這些創新的關鍵，它使用即時數據來監控和優化交通狀況。本研究旨在透過深度學習的應用，創建一個能夠自動辨識違規行為的系統。它不僅檢查交通流的檢測和分析，還研究人類行為實例的識別。車輛辨識、車牌偵測、字元分類都是透過YOLO、WPOD-NET、PaddleOCR等深度學習技術完成的。透過採用先進的偵測技術和違規分析機制，這些系統將能夠在各種環境設定和天氣條件下正確識別車輛和違規行為。這項研究是在越南進行和分析的，與其他東南亞國家相比，越南是一個事故頻繁的國家。此外，研究還建議識別各種交通違規行為，例如闖紅燈、在未經授權的區域停車、超速行駛以及在錯誤車道上行駛。當應用於日常資料集時，實驗的準確率達到了 95% 以上。.

(平裝)Subjects--Topical Terms:

1451528
Paddle OCR.

基於深度學習對越南交通流量檢測與違規行為分析 = = Traffic flow detection and violation analysis in Vietnam based on deep learning /
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245 1 0 $a 基於深度學習對越南交通流量檢測與違規行為分析 = $b Traffic flow detection and violation analysis in Vietnam based on deep learning / $c 潘秋香.
246 1 1 $a Traffic flow detection and violation analysis in Vietnam based on deep learning.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.07.
300 $a [12], 114面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 林彥昆.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學資訊管理系碩士班.
504 $a 含參考書目.
520 3 $a 智慧城市提高了城市地區的效率、永續性和宜居性，從而促進經濟成長並提高公民的生活水平。這使得智慧城市對於國家的發展至關重要。在全球智慧城市領域，整合智慧交通技術對於改善城市和確保高效的交通管理至關重要。動態交通流和管理系統是這些創新的關鍵，它使用即時數據來監控和優化交通狀況。本研究旨在透過深度學習的應用，創建一個能夠自動辨識違規行為的系統。它不僅檢查交通流的檢測和分析，還研究人類行為實例的識別。車輛辨識、車牌偵測、字元分類都是透過YOLO、WPOD-NET、PaddleOCR等深度學習技術完成的。透過採用先進的偵測技術和違規分析機制，這些系統將能夠在各種環境設定和天氣條件下正確識別車輛和違規行為。這項研究是在越南進行和分析的，與其他東南亞國家相比，越南是一個事故頻繁的國家。此外，研究還建議識別各種交通違規行為，例如闖紅燈、在未經授權的區域停車、超速行駛以及在錯誤車道上行駛。當應用於日常資料集時，實驗的準確率達到了 95% 以上。.
520 3 $a The efficiency, sustainability, and livability of urban areas are improved by smart cities, which boost economic growth and raise living standards for citizens. This makes smart cities essential to a nation's development. In the global field of smart cities, integrating intelligent transportation technologies is critical to improving urban mobility and assuring efficient traffic management. Dynamic traffic flow and management systems are the keys to these innovations, using real-time data to monitor and optimize traffic conditions. Through the application of deep learning, the purpose of this research is to create a system that can automatically identify violations. Not only does it examine the detection and analysis of traffic flow, but it also looks into the identification of instances of human behavior. The identification of vehicles, the detection of license plates, and the classification of characters are all accomplished through the utilization of deep learning techniques like YOLO, WPOD-NET, and PaddleOCR. By employing advanced detection technologies and violation analysis mechanisms, these systems will be able to properly identify vehicles and violations across a wide range of ambient settings and weather conditions. The research was conducted and analysis in Vietnam, a country that has a big cause for concern accidents in comparison to other Southeast Asian countries. Additionally, the research suggests recognizing a variety of traffic violations, such as those that include running red lights or parking in unauthorized areas, over limitation, speed, driving in wrong lane. When applied to the daily dataset, the experiment was successful in achieving an accuracy of more than 95%..
563 $a (平裝)
650 # 4 $a Paddle OCR. $3 1451528
650 # 4 $a WPOD-NET. $3 1451527
650 # 4 $a Deep Learning. $3 1127423
650 # 4 $a Traffic Violation Detection. $3 1451526
650 # 4 $a YOLO. $3 1218275
650 # 4 $a 深度學習. $3 1127425
650 # 4 $a 交通違規偵測. $3 1451525
856 7 # $u https://handle.ncl.edu.tw/11296/9mmjhk $z 電子資源 $2 http