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研究生: 王千瑞
Wang, Chien-Jui
論文名稱: 基於深度學習之職安監測系統開發
Development of occupational safety monitoring system based on deep learning
指導教授: 吳順德
Wu, Shuen-De
口試委員: 呂有勝
Lu, Yu-Sheng
劉益宏
Liu, Yi-Hung
吳順德
Wu, Shuen-De
口試日期: 2023/07/13
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 61
中文關鍵詞: 職安監測深度學習人工智慧
英文關鍵詞: Occupational safety monitoring, Deep learning, Artificial intelligence, YOLOv7, LINE Notify
研究方法: 準實驗設計法比較研究
DOI URL: http://doi.org/10.6345/NTNU202300854
論文種類: 學術論文
相關次數: 點閱:95下載:16
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  • 在台灣,每年施工造成意外的比例與職業傷害皆位居前茅,對勞工的生命與產業的生產力造成重大影響,其中勞工不安全行為是意外發生的首要原因。防制此行為的傳統方式是在施工現場架設監視器或派人監工,但由於人力問題,監督的效果與效率並不理想,基於此本研究開發以深度學習為基礎之職安監測系統來協助施工現場的職安管理。
    科技的進步大幅提升影像辨識能力與速度,本研究利用經過模型架構優化和訓練過程優化的新穎物件偵測器YOLOv7,針對施工現場影像進行訓練並建立職安狀態辨識模型後,對施工中的影像進行偵測,將未符合職安規定的事件篩選出來,最後將辨識結果以LINE Notify即時通報。與YOLOv5演算法進行比較,YOLOv7模型在演算法有改進之外,本研究透過訓練資料集的修正與增加以及模型的重新訓練等方式改善職安監測系統的辨識能力,使模型的mAP提升了約4%。
    本研究所建立的辨識模型在訓練階段的最佳mAP@.5高達0.98,此高mAP@.5表示可減少誤報與漏報情況的發生。誤報率太高會造成現場施工的困擾,並對通報失去信心;漏報率太高代表違反職安事件的偵測效果不彰,此將影響即時預警的功能。高mAP@.5所帶來的效益將提升施工現場的安全管理,減少意外的發生,強化本研究在產業實務應用的可行性與價值性。

    In Taiwan, construction accidents and occupational injuries rank among the highest each year, causing significant impact on both workers' lives and industrial productivity. Unsafe behaviors by workers are identified as the primary cause of these accidents. The traditional approach to prevent such behavior involves setting up surveillance cameras or assigning personnel to supervise construction sites. However, due to manpower limitations, the effectiveness and efficiency of supervision are not optimal. Therefore, this study utilizes artificial intelligence recognition models to assist in occupational safety management at construction sites.
    Technological advancements have significantly improved the capabilities and speed of image recognition. In this study, a novel object detector, YOLOv7, optimized in terms of model architecture and training process, was utilized. The detector was trained on construction site images to establish a occupational safety state recognition model. It was then applied to detect and filter out events that did not comply with occupational safety regulations in the construction images. Finally, the recognition results were instantly notified through LINE Notify. Compared to the YOLOv5 algorithm, the YOLOv7 model not only incorporates algorithmic improvements but also enhances the recognition capabilities of the occupational safety monitoring system through modifications and additions to the training dataset, as well as retraining of the model. As a result, the mAP (mean Average Precision) of the model has been improved by approximately 4%.
    The recognition model developed in this study achieved a peak mAP@.5 of 0.98 during the training phase. This high mAP@.5 value indicates a reduced occurrence of false positives and false negatives. Excessive false positives can cause disruptions at the construction site and undermine confidence in the notification system, while a high false negative rate implies ineffective detection of occupational safety violations, which compromises the real-time alerting functionality. The benefits of a high mAP@.5 value include improved safety management at construction sites, reduced incidents, and enhanced feasibility and value of practical application in the industry.

    摘要 i Abstract ii 致謝 iv 目錄 v 表目錄 vii 圖目錄 viii 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目標 1 1.3 文獻探討 3 1.3.1 YOLO演進 4 1.3.2 VoVNet架構 5 1.3.3 CSPVoVNet架構 6 1.4 研究方法 6 1.5 章節介紹 7 第二章 職安模型架構 9 2.1 模型訓練流程 9 2.2 模型類別定義 10 2.3 篩選、標注照片與模型偵測 12 第三章 卷積神經網路模型 16 3.1 卷積神經層概論 16 3.1.1 機器學習 16 3.1.2 深度學習 20 3.1.3 卷積神經層的演進 22 3.2 CNN概論 25 第四章 YOLOv7模型訓練 29 4.1 YOLOv7模型架構 29 4.2 模型評估 35 4.2.1 IoU 35 4.2.2 NMS 37 4.2.3 混淆矩陣 38 4.2.4 mAP 39 4.3 邊緣運算的應用 40 4.3.1 Jetson Nano v3開發套件 41 4.3.2 開發套件的環境架設 42 4.3.3 開發套件即時偵測與結果回傳 43 第五章 實驗過程與結果 44 5.1 職安模型之偵測和訓練結果 44 5.1.1 蒐集訓練用資料 44 5.1.2 資料集的修正 46 5.2 即時辨識系統之狀態及警報頁面 52 第六章 結論與未來展望 55 6.1 結論 55 6.2 未來展望 55 參考文獻 57

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