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研究生： 邱品閎
Chou, Pin-Hong
論文名稱： 以卷積神經網路即時偵測機車騎士安全帽的違規事件
Real-Time Detection of Motorcycle Rider's Helmet Violation by Using Convolutional Neural Network
指導教授： 周榮華
Chou, Jung-Hua
學位類別： 碩士
Master
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2025
畢業學年度： 113
語文別： 中文
論文頁數： 169
中文關鍵詞： 影像辨識 、卷積神經網路 、多目標追蹤 、安全帽違規檢測
外文關鍵詞： Image Recognition, Convolution Neural Network, Multiple-Object Tracking, Helmet Violation Detection
相關次數： 點閱：26  下載：9 
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    • 根據交通部統計，2024 年共有 2,950 名因交通事故喪生，其中高達 1,858 人是機車騎士。事故分析更顯示，30 日內死亡案件中，有超過 8 成死者為頭部外傷。並且未佩戴安全帽者的致死率為正確佩戴者的 8.63 倍，顯示安全帽使用與交通事故傷亡具有高度關聯性。警政署統計同年度共取締 189,566 件「未戴安全帽」之交通違規事件，顯示針對此類違規行為進行即時監測與自動化辨識，具有急迫性與重要性。
    本研究旨在開發一套基於深度學習之機車騎士未戴安全帽自動檢測系統。系統首先採用 YOLO 系列物件偵測模型進行安全帽與頭部的偵測與分類，並對原始自然影像進行資料擴增處理，以提升辨識準確率。接著，透過多目標追蹤演算法對移動物件進行連續追蹤，並自動擷取違規行為畫面以利後續驗證與紀錄。
    實驗資料來自公開網路與自行拍攝影像，總計 4,627 張影像，經資料擴增達到 12,865 張，其中訓練集為 12,395 張，測試集為 470 張。在靜態影像辨識實驗中，YOLOv8n 模型在佩戴安全帽、未佩戴安全帽、機車騎士和自行車騎士四種類別上，平均準確率（mAP）達 91.9%。於物件追蹤測試中，使用共 39 支實際道路影片進行驗證，當連續偵測幀數達 15 幀時，結果顯示誤判為未戴安全帽之錯誤率由 2.08%降至 1.22%，誤判成機車騎士之錯誤率由 1.84% 降至 1.33%，驗證系統於實務場域中具備高度準確性與應用潛力。

    In 2024, 2,950 motorcyclists died in traffic accidents in Taiwan, counted as deaths occurring within 30 days after the accident, of which 1,858 were motorcycle riders. More than 80% of these fatalities involved head injuries, and the fatality rate among riders without helmets was 8.63 times higher than that of helmeted riders. The National Police Agency reported 189,566 violations for “not wearing a helmet,” underscoring the urgent need for real-time automated detection.
    This study develops a deep learning-based system to detect motorcycle riders without helmets. YOLO-series object detection models with convolution neural network identify helmets and heads via image recognition, with data augmentation applied to improve accuracy. A multi-object tracking algorithm continuously tracks moving objects and captures violation frames for verification.
    Using 12,395 training images and 470 test images, the YOLOv8n model achieves a mean Average Precision (mAP) of 91.9%. In tracking tests with 39 real-world traffic videos, when consecutive detection reaching 15 frames, error rates of falsely classifying riders as being unhelmeted and objects as motorcyclists decrease from 2.08% to 1.22% and 1.84% to 1.33%, respectively, demonstrating high accuracy and practical applicability in real-world scenarios of the present approach.

    摘要 I EXTENDED ABSTRACT II 致謝 V 目錄 VI 表目錄 IX 圖目錄 XI 第一章　緒論 1 1.1　研究動機與背景 1 1.2　研究目的 2 第二章　文獻探討 4 2.1　安全帽與騎乘工具相關研究 4 2.2　交通安全辨識相關領域研究文獻 10 2.2.1　資料擴增 10 2.2.2　深度學習演算法 11 2.3　多目標物件追蹤 12 2.4　卷積神經網路 13 2.4.1　卷積層（Convolutional Layer） 14 2.4.2　池化層（Pooling Layer） 17 2.4.3　全連接層（Fully Connected Layer） 18 2.5　光學字元辨識相關研究 19 第三章　安全帽與機車辨識系統 20 3.1　影像辨識模型 21 3.2　YOLO 設計概念 22 3.3　YOLO CIoU 非最大抑制（CIoU Loss Non-Maximum Suppression） 24 3.3.1　IoU（Intersection over Union） 24 3.3.2　CIoU（Complete Intersection over Union） 25 3.4　YOLOv8 網路架構 27 3.5　訓練階段 33 3.5.1　資料集蒐集作業 33 3.5.2　影像標記作業 37 3.5.3　數據集管理 38 3.5.4　參數選擇 40 3.6　辨識系統模型介紹 42 3.6.1　多目標物件追蹤模型 44 3.6.2　多目標物件追蹤設計概念 45 3.6.3　多幀判定與違規紀錄 49 第四章　實驗結果與討論 51 4.1　實驗影像資料集 51 4.2　實驗設備及訓練參數設定 53 4.3　實驗結果衡量指標 54 4.4　實驗結果 57 4.4.1　YOLOv8 各模型性能 57 4.4.2　YOLOv8 各模型性能比較 67 4.4.3　測試集辨識結果 69 4.4.4　實際道路影片辨識結果 73 4.4.5　實際道路影片追蹤測試結果 127 第五章　結論與建議 135 5.1　結論 135 5.2　建議 136 參考文獻 138

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