基于深度學(xué)習(xí)的交通視頻檢測(cè)及車型分類研究
發(fā)布時(shí)間：2018-11-18 13:10
【摘要】：隨著汽車保有量的急劇增加,交通問(wèn)題越來(lái)越突出。與此同時(shí),在互聯(lián)網(wǎng)大數(shù)據(jù)時(shí)代的背景下,深度學(xué)習(xí)獲得了迅猛發(fā)展,給模式識(shí)別任務(wù)帶來(lái)了巨大的變革,它還給許多領(lǐng)域提供了一種新的解決方案。因此,將深度學(xué)習(xí)應(yīng)用到解決交通問(wèn)題已經(jīng)成為一種研究趨勢(shì)。本文利用深度學(xué)習(xí)中的卷積神經(jīng)網(wǎng)絡(luò)方法來(lái)解決交通視頻中的交通目標(biāo)檢測(cè)及車型分類問(wèn)題,為智能交通系統(tǒng)提供技術(shù)支持從而緩解交通擁堵等問(wèn)題。本文主要內(nèi)容如下:首先介紹了深度學(xué)習(xí)的基本模型,主要分為深度置信網(wǎng)絡(luò)、棧式自編碼網(wǎng)絡(luò)和卷積神經(jīng)網(wǎng)絡(luò),主要重點(diǎn)研究了卷積神經(jīng)網(wǎng)絡(luò)的構(gòu)成、卷積神經(jīng)網(wǎng)絡(luò)區(qū)別于傳統(tǒng)神經(jīng)網(wǎng)絡(luò)的特點(diǎn),以及卷積神經(jīng)網(wǎng)絡(luò)的訓(xùn)練機(jī)制。針對(duì)利用人工設(shè)計(jì)的學(xué)習(xí)特征進(jìn)行交通目標(biāo)檢測(cè)時(shí),會(huì)存在學(xué)習(xí)特征設(shè)計(jì)過(guò)程繁瑣、適應(yīng)范圍受限制等問(wèn)題,本文采用卷積神經(jīng)網(wǎng)絡(luò)來(lái)自動(dòng)提取特征。以基于區(qū)域的卷積神經(jīng)網(wǎng)絡(luò)(RCNN)為基礎(chǔ),設(shè)計(jì)了交通視頻檢測(cè)方案,結(jié)合了Fast RCNN框架和RPN區(qū)域建議網(wǎng)絡(luò)的優(yōu)點(diǎn)。針對(duì)交通目標(biāo)輪廓形狀各異的特點(diǎn),本文對(duì)交通視頻檢測(cè)網(wǎng)絡(luò)中的共享卷積網(wǎng)絡(luò)進(jìn)行了改進(jìn),主要是加深了卷積網(wǎng)絡(luò)的深度,從5層卷積加深到13層。在交通訓(xùn)練樣本中取得了較好的效果,交通目標(biāo)的平均檢測(cè)率提升了超過(guò)3%。針對(duì)已有的車型分類手段只將車輛進(jìn)行粗略分類,已經(jīng)無(wú)法滿足車聯(lián)網(wǎng)對(duì)車輛信息需求的問(wèn)題,本文采用深度殘差神經(jīng)網(wǎng)絡(luò)對(duì)車型進(jìn)行精細(xì)型分類,車輛品牌可達(dá)64種,車型可達(dá)281種。在設(shè)計(jì)車型分類網(wǎng)絡(luò)的過(guò)程中,分析了常用圖像分類卷積神經(jīng)網(wǎng)絡(luò),并在兩套數(shù)據(jù)集上進(jìn)行了性能對(duì)比,最終選擇了深度殘差網(wǎng)絡(luò)作為車型分類網(wǎng)絡(luò)的主體框架。利用標(biāo)準(zhǔn)車型數(shù)據(jù)集CompCars對(duì)車型分類網(wǎng)絡(luò)進(jìn)行可學(xué)習(xí)參數(shù)微調(diào),訓(xùn)練后的車型分類網(wǎng)絡(luò)的前五準(zhǔn)確率在CompCars數(shù)據(jù)集上可達(dá)97.3%,在Vehicle ID數(shù)據(jù)集上可達(dá)89.4%,驗(yàn)證了車型分類網(wǎng)絡(luò)的有效性。最后,對(duì)本文設(shè)計(jì)的基于交通視頻的檢測(cè)網(wǎng)絡(luò)和車型分類網(wǎng)絡(luò)分別在圖像和視頻上進(jìn)行了檢驗(yàn)。檢測(cè)網(wǎng)絡(luò)能在晴天、黑夜、雨天和擁堵等不同狀態(tài)獲得較高的檢測(cè)率,在有效視野中車輛檢測(cè)率最高可達(dá)98.7%,并具有一定的魯棒性。分類網(wǎng)絡(luò)在基于視頻產(chǎn)生的車輛圖像測(cè)試集中,獲得了最高達(dá)到88%的前五準(zhǔn)確率。實(shí)驗(yàn)結(jié)果表明,本文所設(shè)計(jì)的檢測(cè)網(wǎng)絡(luò)和分類網(wǎng)絡(luò)具有一定的實(shí)用價(jià)值。
[Abstract]:With the rapid increase of vehicle ownership, traffic problems become more and more prominent. At the same time, under the background of Internet big data era, in-depth learning has developed rapidly, which has brought a great change to the task of pattern recognition. It also provides a new solution in many fields. Therefore, the application of deep learning to solve traffic problems has become a research trend. In this paper, the convolution neural network method in depth learning is used to solve the traffic target detection and vehicle classification problems in traffic video, and to provide technical support for intelligent transportation system to alleviate traffic congestion and so on. The main contents of this paper are as follows: firstly, the basic model of deep learning is introduced, which is divided into three parts: depth confidence network, stack self-coding network and convolutional neural network. Convolutional neural networks are different from traditional neural networks and the training mechanism of convolutional neural networks. In order to solve the problem that the design process of learning features is cumbersome and the scope of adaptation is limited when using artificial design learning features to detect traffic targets, this paper uses convolution neural network to extract features automatically. Based on the area-based convolution neural network (RCNN), a traffic video detection scheme is designed, which combines the advantages of the Fast RCNN framework and the RPN regional recommendation network. In this paper, the shared convolution network in the traffic video detection network is improved, which mainly deepens the depth of the convolutional network, from five layers to 13 layers. Good results were obtained in traffic training samples, and the average detection rate of traffic targets increased by more than 3 percent. In view of the existing vehicle classification methods only rough classification of vehicles, can no longer meet the needs of vehicle information, this paper uses the depth residual neural network for fine classification of vehicle models, vehicle brands can reach 64, There are 281 types of models. In the course of designing the vehicle classification network, the neural network of image classification is analyzed, and the performance of the two sets of data sets is compared. Finally, the depth residual network is chosen as the main frame of the vehicle classification network. The model classification network can be fine-tuned by using the standard model data set (CompCars). The first five accuracy rates of the trained vehicle classification network can reach 97.3 on the CompCars data set and 89.4 on the Vehicle ID data set. The validity of vehicle classification network is verified. Finally, the detection network based on traffic video and vehicle classification network designed in this paper are tested on image and video, respectively. The detection network can obtain high detection rate in different states such as sunny, dark, rainy and congested. In the effective field of vision, the vehicle detection rate can be up to 98.775, and it is robust to a certain extent. In the vehicle image test set based on video, the first five accuracy rates of classification network are up to 88%. The experimental results show that the detection network and classification network designed in this paper have some practical value.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP183
本文編號(hào)：2340119
[Abstract]:With the rapid increase of vehicle ownership, traffic problems become more and more prominent. At the same time, under the background of Internet big data era, in-depth learning has developed rapidly, which has brought a great change to the task of pattern recognition. It also provides a new solution in many fields. Therefore, the application of deep learning to solve traffic problems has become a research trend. In this paper, the convolution neural network method in depth learning is used to solve the traffic target detection and vehicle classification problems in traffic video, and to provide technical support for intelligent transportation system to alleviate traffic congestion and so on. The main contents of this paper are as follows: firstly, the basic model of deep learning is introduced, which is divided into three parts: depth confidence network, stack self-coding network and convolutional neural network. Convolutional neural networks are different from traditional neural networks and the training mechanism of convolutional neural networks. In order to solve the problem that the design process of learning features is cumbersome and the scope of adaptation is limited when using artificial design learning features to detect traffic targets, this paper uses convolution neural network to extract features automatically. Based on the area-based convolution neural network (RCNN), a traffic video detection scheme is designed, which combines the advantages of the Fast RCNN framework and the RPN regional recommendation network. In this paper, the shared convolution network in the traffic video detection network is improved, which mainly deepens the depth of the convolutional network, from five layers to 13 layers. Good results were obtained in traffic training samples, and the average detection rate of traffic targets increased by more than 3 percent. In view of the existing vehicle classification methods only rough classification of vehicles, can no longer meet the needs of vehicle information, this paper uses the depth residual neural network for fine classification of vehicle models, vehicle brands can reach 64, There are 281 types of models. In the course of designing the vehicle classification network, the neural network of image classification is analyzed, and the performance of the two sets of data sets is compared. Finally, the depth residual network is chosen as the main frame of the vehicle classification network. The model classification network can be fine-tuned by using the standard model data set (CompCars). The first five accuracy rates of the trained vehicle classification network can reach 97.3 on the CompCars data set and 89.4 on the Vehicle ID data set. The validity of vehicle classification network is verified. Finally, the detection network based on traffic video and vehicle classification network designed in this paper are tested on image and video, respectively. The detection network can obtain high detection rate in different states such as sunny, dark, rainy and congested. In the effective field of vision, the vehicle detection rate can be up to 98.775, and it is robust to a certain extent. In the vehicle image test set based on video, the first five accuracy rates of classification network are up to 88%. The experimental results show that the detection network and classification network designed in this paper have some practical value.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【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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