發(fā)布時間：2018-03-30 07:04

  本文選題：煤礦安全生產(chǎn)　切入點：計算機視覺　出處：《太原科技大學(xué)》2017年碩士論文

【摘要】：視頻監(jiān)控是安全防范的重要手段,近年來視頻監(jiān)控技術(shù)在礦井生產(chǎn)中得到廣泛應(yīng)用,為煤礦安全生產(chǎn)提供技術(shù)上的支持和保證。目前我國礦井下的視頻監(jiān)控系統(tǒng)主要以人工監(jiān)視為主,經(jīng)常出現(xiàn)人為的錯漏現(xiàn)象。隨著計算機視覺技術(shù)的發(fā)展,煤礦智能視頻監(jiān)控將取代人工監(jiān)控的方式實現(xiàn)對異常狀態(tài)的實時自動監(jiān)測。帶式輸送機作為礦井生產(chǎn)中應(yīng)用最多的運輸設(shè)備,在長時間高強度運轉(zhuǎn)下極易出現(xiàn)各種故障,也是煤礦井下視頻監(jiān)控的重點。本文研究了基于視覺技術(shù)的礦井下帶式輸送機異常狀態(tài)監(jiān)測方法,以實現(xiàn)帶式輸送機的智能監(jiān)控,具體研究內(nèi)容如下:首先,為解決煤礦井下視頻監(jiān)控系統(tǒng)采集到的圖像對比度低、光照不均且伴有大量噪聲等視覺效果差的問題,給出了一種基于加權(quán)引導(dǎo)濾波同步去噪的單尺度Retinex算法實現(xiàn)煤礦井下圖像增強。利用加權(quán)引導(dǎo)濾波代替單尺度Retinex算法的高斯濾波對圖像的低頻分量進(jìn)行照度估計,再經(jīng)對數(shù)域轉(zhuǎn)換到實數(shù)域得到反射圖像,最后利用帶有保邊去噪功能的加權(quán)引導(dǎo)濾波對圖像的高頻分量進(jìn)行去噪處理得到增強后的圖像。然后針對帶式輸送機的輸送帶跑偏故障,給出了一種基于計算機視覺的輸送帶跑偏監(jiān)測方法。首先將視頻監(jiān)控中采集到的視頻圖像設(shè)置感興趣區(qū)域(Region Of Interest,ROI)以減少計算量,同時對ROI進(jìn)行圖像預(yù)處理。然后采用改進(jìn)的Canny邊緣檢測算法得到ROI邊緣二值圖像,利用累計概率霍夫變換(PPHT)提取輸送帶邊緣直線特征,最后根據(jù)所得直線特征來判斷輸送帶是否跑偏。最后針對帶式輸送機在運送物料過程中的打滑故障,給出了一種基于OpenCV的帶式輸送機打滑檢測方法。首先利用背景差分法和連通區(qū)域標(biāo)記法檢測出輸送帶上的多個運動目標(biāo),其次使用最小外接矩形獲得目標(biāo)的寬高比,結(jié)合基于質(zhì)心特征的軌跡跟蹤方法獲得多個運動目標(biāo)的位移及對應(yīng)的時間間隔,最后利用速度公式獲得輸送帶的運行速度,從而判斷是否存在打滑故障。在經(jīng)過了大量實驗并對實驗數(shù)據(jù)進(jìn)行全面分析后,實驗結(jié)果表明本文提出的基于視覺技術(shù)的礦井下帶式輸送機異常狀態(tài)監(jiān)測方法可以實現(xiàn)對跑偏和打滑故障的自動監(jiān)測,對提高我國煤礦安全生產(chǎn)監(jiān)控信息化水平具有重要意義。
[Abstract]:Video surveillance is an important means of safety prevention. In recent years, video surveillance technology has been widely used in mine production, providing technical support and guarantee for coal mine safety production.At present, the video surveillance system under the mine in our country is mainly manual surveillance, which often appears the phenomenon of human error and leakage.With the development of computer vision technology, intelligent video surveillance in coal mine will replace manual monitoring to realize real-time and automatic monitoring of abnormal state.Belt conveyor, as the most used transportation equipment in mine production, is prone to various faults under long time and high intensity operation, which is also the focus of video monitoring in coal mine.A single scale Retinex algorithm based on weighted guided filter for simultaneous de-noising is presented to enhance the underground image of coal mine.Using weighted guided filter instead of Gao Si filter of single-scale Retinex algorithm to estimate the illumination of the low-frequency component of the image, the reflected image is obtained by converting the logarithmic domain to the real domain.Finally, the enhanced image is obtained by using the weighted guided filter with edge-preserving denoising function to Denoise the high-frequency components of the image.Then, a method of belt deviation monitoring based on computer vision is presented in view of belt deviation fault of belt conveyor.Firstly, the region of interest (region of interest) is set up in the video surveillance to reduce the computational cost, and the ROI is preprocessed at the same time.Then the improved Canny edge detection algorithm is used to get the binary image of the ROI edge, and the cumulative probability Hough transform is used to extract the edge line feature of the conveyor belt. Finally, according to the obtained straight line feature, the conveyor belt is judged whether it is running out of direction or not.Finally, aiming at the slip fault of belt conveyor in the process of transporting materials, a method of skid detection based on OpenCV is presented.First, the background difference method and the connected area marking method are used to detect multiple moving targets on the conveyor belt, and then the minimum outer rectangle is used to obtain the ratio of the width to the height of the target.Combined with the trajectory tracking method based on the centroid feature, the displacement and the corresponding time interval of multiple moving objects are obtained. Finally, the speed of the conveyor belt is obtained by using the velocity formula, so as to judge whether there is a slip fault or not.After a large number of experiments and a comprehensive analysis of the experimental data, the experimental results show that the method proposed in this paper can automatically monitor the deviation and slip faults of the mine belt conveyor based on visual technology.It is of great significance to improve the information level of coal mine safety production monitoring in China.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD528.1;TP391.41

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本文編號：1684842