本文選題：井下行人檢測　切入點：卷積神經(jīng)網(wǎng)絡　出處：《西安科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：煤炭在我國能源利用中占據(jù)著舉足輕重的地位,煤礦安全尤其是井下生產(chǎn)環(huán)境的安全則一直是煤礦行業(yè)的重中之重。目前煤礦企業(yè)對于井下工作人員的檢測主要依托于已裝備的井下人員定位系統(tǒng)等,這些技術的應用可以有效地進行人員的定位和識別,但是在使用過程當中也出現(xiàn)替下、捎卡等情況,其精準度不高,智能化水平較低,特別是當監(jiān)控人員疏忽時,存在很大的安全隱患�；谶@樣的背景,本文結合DCNN(深度卷積神經(jīng)網(wǎng)絡)在視頻圖像識別領域中的應用和井下裝備的工業(yè)視頻監(jiān)控系統(tǒng),提出了一種基于DCNN的礦井井下行人檢測技術。為提高檢測速度,采用了 YOLO目標檢測系統(tǒng),并針對井下特殊環(huán)境的特點對其進行了改進,最終利用Java Web技術對基于改進YOLO的井下行人檢測系統(tǒng)進行了簡單實現(xiàn)。本文以神經(jīng)網(wǎng)絡為基礎,首先對卷積神經(jīng)網(wǎng)絡、深度學習網(wǎng)絡等的理論做了介紹與分析,在深度卷積神經(jīng)網(wǎng)絡的基礎上對YOLO目標檢測系統(tǒng)的網(wǎng)絡結構以及檢測過程等原理進行了詳細的剖析,分析了 YOLO系統(tǒng)精確度不高的缺陷,針對礦井下的視頻質(zhì)量差、背景單調(diào)、檢測目標單一等特點對原有的YOLO系統(tǒng)在數(shù)據(jù)集和網(wǎng)絡結構上進行了改進。利用煤礦井下的監(jiān)控視頻重新制作了訓練集,網(wǎng)絡結構上利用淺層的表征信息與深層的語義信息相結合的思想將網(wǎng)絡中第八層的特征提取出來與最后層的輸出相加作為整個網(wǎng)絡最后的輸出,在提取第八層提取特征的基礎上提.提出了三種方案,分別為先卷積后采樣、先采樣后卷積、最后層輸出利用反卷積擴大特征圖再與第八層相加。通過在Caffe框架上進行實驗并分析結果,綜合考慮后選擇了第二種方案為最終改進方案,證明.了改進后的YOLO系統(tǒng)在井下特殊環(huán)境的行人檢測性能得到了提升。最后,利用Java EE技術構建了關于Java Web的井下行人檢測系統(tǒng),該系統(tǒng)包含系統(tǒng)管理、權限管理、檢測管理、考勤信息、設備管理五個模塊,對DCNN的井下行人檢測系統(tǒng)進行了測試分析及功能性驗證,說明了所設計系統(tǒng)的可行性。通過本文的實驗可以看出,改進后的YOLO系統(tǒng)對井下特殊環(huán)境的檢測有比較好的檢測效果。
[Abstract]:Coal occupies a pivotal position in the utilization of energy in China. Coal mine safety, especially the safety of the underground production environment, has always been the top priority of the coal mining industry. At present, the inspection of underground workers by coal mining enterprises mainly depends on the positioning system of the underground personnel that has been equipped. The application of these technologies can effectively locate and identify the personnel, but in the process of use, there are replacement, cards, etc., their accuracy is not high, and the level of intelligence is low, especially when the monitoring personnel are negligent. Based on this background, this paper combines the application of DCNN (depth convolution neural network) in the field of video image recognition and the industrial video surveillance system of underground equipment. This paper presents a kind of underground pedestrian detection technology based on DCNN. In order to improve the detection speed, the YOLO target detection system is adopted, and it is improved according to the characteristics of the special underground environment. Finally, using Java Web technology, a simple realization of underground pedestrian detection system based on improved YOLO is carried out. Firstly, the theory of convolution neural network and depth learning network is introduced and analyzed based on neural network. On the basis of deep convolution neural network, the network structure and detection process of YOLO target detection system are analyzed in detail, and the defects of low accuracy of YOLO system are analyzed. The video quality under mine is poor and the background is monotonous. The original YOLO system has been improved in data set and network structure with the characteristics of single detection target, and the training set has been remade by using the monitoring video of underground coal mine. In the network structure, the feature of the eighth layer in the network is extracted and the output of the last layer is added as the final output of the whole network by the idea of combining the shallow representation information with the deep semantic information. On the basis of extracting features from the eighth layer, three schemes are proposed, which are first convolution and then sampling, first sampling and then convolution. The final layer output uses deconvolution expanded feature map to add to the eighth layer. Through the experiment on the Caffe framework and the analysis of the results, the second scheme is selected as the final improvement scheme. It is proved that the improved YOLO system has improved the performance of pedestrian detection in the special underground environment. Finally, the underground pedestrian detection system about Java Web is constructed by using Java EE technology. The system includes system management, authority management, detection management, etc. Five modules of attendance information and equipment management are used to test and analyze the underground pedestrian detection system of DCNN and verify the function of the system. The feasibility of the designed system is demonstrated by the experiment in this paper. The improved YOLO system has a good effect on the detection of underground special environment.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD76;TP391.41

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