基於卷積神經網路及色彩影像技術之火焰辨識

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姓名

周哲伍(Che-Wu Chou) 查詢紙本館藏

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電機工程學系

論文名稱

基於卷積神經網路及色彩影像技術之火焰辨識
(Flame Identification Based on Convolutional Neural Network and Color Image Technology)

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摘要(中)

中文摘要
本篇論文提出一種深度學習的演算法藉由卷積神經網路演算法以及色彩空間分析，同時結合了背景相減法、形態學、角點偵測以及感興趣區域，來實現連續影像火焰追蹤辨識。
近幾年，智慧型家庭監控系統的崛起並且基於即時影像的技術更加成熟，所以對於火焰的防範，有別於過去較傳統的方式來偵測，例如煙霧或溫度感測器，但相較於即時影像偵測，大多為時已晚。就如近期有一場火災奪走七條消防員的生命，如果有此監控影像的使用，也許就不會有這個悲劇發生，因此本文所探討的系統是希望在火焰開始擴展以前，藉由即時影像進行火焰辨識，進而告知人們，以至於達到降低人員及財物的損失。

摘要(英)

Abstract
This paper proposes a deep learning algorithm by using “convolutional neural network algorithm” and “color space analysis” for real-time recognition, which combines background substraction, morphology, corner detection and regions of interest to achieve the flame tracking recognition of continuous image.
In recent years, the rise of smart home monitoring systems and the technology based on real-time image have become more and more mature, so the preventive measure of fire is very different from traditional methods, such as smoke sensors and temperature sensors. Therefore, compared to the real-time image detection, the traditional methods are too slow to measure the prevention. As it happened recently, there are seven firefighters died on the fire disaster. If the surveillance image is used, there may not be this tragedy. As a consequence, the system discussed in this article can notify people by using real-time image to flame recognition before the flame begin to expand, so as to reduce the loss of personnel and property.

關鍵字(中)

★ 火焰辨識
★ 視訊監控
★ 卷積神經網路

關鍵字(英)

★ flame recognition
★ video surveillance
★ convolutional neural network

論文目次

目錄
中文摘要 I
Abstract II
致謝 IV
目錄 V
圖目錄 VIII
表目錄 XII
第一章緒論 1
1.1研究目的 1
1.2 研究背景 2
1.3 研究方法 4
1.4 主要結果與貢獻 4
1.5論文架構 5
第二章系統架構與系統描述 6
2.1外部硬體 6
2.2內部軟體 7
2.3 系統架構 9
第三章火焰偵測 13
3.1 色彩空間介紹與應用 14
3.1.1 色彩空間 14
3.1.2 火焰偵測 16
3.2 影像及形態學處理 21
3.2.1 中值濾波 22
3.2.2 高斯濾波 23
3.2.3 侵蝕(Erosion) 28
3.2.4 膨脹(Dilation) 29
3.3 目標偵測 30
3.3.1 連續影像相減法（Temporal difference） 31
3.3.2 光流法（Optical flow） 31
3.3.3 卡爾曼波器(Kalman filter) 32
3.3.4 高斯混合(Mixture of Gaussians) (MoG) 32
3.3.5 背景相減法(Background substraction) 32
3.4 火焰特徵分析 34
3.5 感興趣區域與影像處理結果 35
第四章卷積神經網路演算法 38
4.1 卷積神經網路組成 38
4.1.1 卷積層(Convolution Layer) 39
4.1.2 池化層(Pooling Layer) 41
4.1.3 全連接層(Fully Connected Layer) 42
4.2 卷積神經網路種類 43
4.2.1 Lenet 43
4.2.2 AlexNet 44
4.2.3 GoogleNet 45
4.2.4 VGGNET 46
4.3 卷積神經網路架構 46
4.4　系統流程說明 50
第五章實驗結果與討論 51
5.1 火焰偵測情況 51
5.1.1 光線不同實驗 51
5.1.2 背景不同實驗 55
5.2 卷積神經網路 58
5.3 討論 60
第六章結論與建議 62
6.1 結論 62
6.2 建議 63
參考文獻 64

參考文獻

參考文獻
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指導教授

鍾鴻源莊堯棠(Hung-Yuan Chung Yau-Tarng Juang)

審核日期

2018-7-25

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