本文選題：深度學(xué)習(xí) + 卷積神經(jīng)網(wǎng)絡(luò)��； 參考：《廣東技術(shù)師范學(xué)院》2017年碩士論文

【摘要】：卷積神經(jīng)網(wǎng)絡(luò)是一種源于人工神經(jīng)網(wǎng)絡(luò)的深度學(xué)習(xí)方法。它具有局部連接、權(quán)值共享的特點(diǎn),同時(shí)能夠?qū)崿F(xiàn)特征的自動(dòng)提取,它改善了傳統(tǒng)模式識(shí)別方法中特征提取難的問(wèn)題,因此卷積神經(jīng)網(wǎng)絡(luò)廣泛應(yīng)用于自然語(yǔ)言處理、語(yǔ)音識(shí)別、推薦系統(tǒng)、計(jì)算機(jī)視覺(jué)等領(lǐng)域�；隈{駛員外部特征的疲勞駕駛檢測(cè)技術(shù)在多個(gè)方面取得了一定的進(jìn)展,但是駕駛員臉部特征提取的方法有待進(jìn)一步提高,同時(shí)駕駛員眼睛定位的時(shí)間較長(zhǎng),影響系統(tǒng)識(shí)別速率。論文將卷積神經(jīng)網(wǎng)絡(luò)應(yīng)用于人臉識(shí)別,對(duì)瞳孔定位算法進(jìn)行改進(jìn),有效地克服了原算法計(jì)算量大的問(wèn)題,根據(jù)駕駛員眼睛在不同狀態(tài)下寬與高比例不同的特點(diǎn),實(shí)現(xiàn)了一種簡(jiǎn)單可行的眼睛狀態(tài)判斷方法,并通過(guò)PERCLOS算法對(duì)駕駛員的疲勞狀態(tài)進(jìn)行判定。應(yīng)用卷積神經(jīng)網(wǎng)絡(luò)模型對(duì)ORL人臉庫(kù)實(shí)驗(yàn)得到識(shí)別率為85%,識(shí)別平均時(shí)間為20ms。改進(jìn)的Hough變換方法對(duì)駕駛員眼睛的定位準(zhǔn)確率和平均時(shí)間分別為92%和29ms,對(duì)駕駛員眼睛狀態(tài)的判斷正確率為83.9%。論文使用的疲勞駕駛檢測(cè)方法能比傳統(tǒng)的檢測(cè)方法取得更好的效果,設(shè)計(jì)基于人臉識(shí)別的疲勞駕駛檢測(cè)原型系統(tǒng),實(shí)現(xiàn)了駕駛員臉部特征檢測(cè)、眼睛定位、眼睛狀態(tài)判斷、疲勞判定等功能。實(shí)驗(yàn)結(jié)果表明,系統(tǒng)對(duì)疲勞的識(shí)別率為87.5%,疲勞判斷的響應(yīng)時(shí)間為17ms,有較好的實(shí)際應(yīng)用價(jià)值。
[Abstract]:Convolutional neural network is a kind of deep learning method derived from artificial neural network.It has the characteristics of local connection, weight sharing and automatic feature extraction. It improves the difficulty of feature extraction in traditional pattern recognition methods. Therefore, convolution neural network is widely used in natural language processing and speech recognition.Recommendation system, computer vision and other fields.Fatigue driving detection technology based on driver's external features has made some progress in many aspects, but the method of driver's facial feature extraction needs to be further improved, and the driver's eye location time is longer.It affects the recognition rate of the system.In this paper, the convolution neural network is applied to face recognition, and the pupillary location algorithm is improved, which effectively overcomes the problem that the original algorithm has a large amount of computation. According to the characteristics of the driver's eyes in different states, the width and the proportion of the eyes are different.A simple and feasible method for judging eye state is implemented, and the fatigue state of driver is judged by PERCLOS algorithm.By using convolutional neural network model, the recognition rate of ORL face database is 85% and the average recognition time is 20 Ms.The accuracy and average time of the improved Hough transform for eye localization are 92% and 29 msrespectively, and the correct rate for judging the eye state of the driver is 83.9%.The fatigue driving detection method used in this paper can achieve better results than the traditional detection method. A prototype system of fatigue driving detection based on face recognition is designed, which realizes driver's face feature detection, eye location, eye state judgment.Fatigue judgment and other functions.The experimental results show that the fatigue recognition rate of the system is 87.5 and the response time of fatigue judgment is 17mswhich has good practical application value.
【學(xué)位授予單位】：廣東技術(shù)師范學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP183

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本文編號(hào)：1731610