【摘要】：輸送機是現(xiàn)代采礦工業(yè)的重要運輸工具。在煤炭運輸中,煤矸石、金屬材料等堅硬物質(zhì)經(jīng)�；旌显诿禾恐�,可能會使得輸送帶產(chǎn)生縱向撕裂,這樣的猝發(fā)事故通常會導致采礦設(shè)備和生產(chǎn)的停工,造成巨大的經(jīng)濟損失,因此我們需要實時、可靠的檢測輸送帶縱向撕裂。近年來,由于機器視覺可以提高檢測效率和精度,在輸送帶故障檢測中,視覺檢測成為一個重要的研究方向。本文基于機器視覺,綜合紅外成像技術(shù)和可見光成像技術(shù)的互補特性,提出了一種基于紅外與可見光融合的輸送帶縱向撕裂一體化雙目視覺檢測方法,并依據(jù)輸送帶縱向撕裂視覺檢測方法現(xiàn)有問題,提出本文檢測方法的設(shè)計方案,主要包括三部分:輸送帶圖像采集,輸送帶圖像預處理,輸送帶圖像縱向撕裂特征提取與識別。在輸送帶圖像采集中,基于紅外與可見光融合技術(shù),論文提出一種新的一體化雙目視覺傳感裝置采集紅外與可見光融合圖像,該傳感裝置通過棱鏡分光的方法將從同一鏡頭入射的同軸光分為紅外光和可見光,并分別進入兩個感光芯片,可以同時捕獲同一場景下的紅外信息和可見光信息,在圖像融合前無需配準處理。本文研究了該傳感裝置的成像原理,并在MATLAB上對其成像過程進行理論仿真研究,驗證其可行性和有效性。由于論文只對提出的一體化雙目視覺傳感裝置進行了理論仿真研究,因此在輸送帶圖像采集實驗部分,首先采用紅外相機和可見光相機分別采集紅外圖像和可見光圖像,然后再進行紅外與可見光圖像的配準融合,最終實現(xiàn)輸送帶紅外與可見光融合圖像的采集。在實驗室搭建圖像采集實驗平臺,實現(xiàn)輸送帶在撕裂狀態(tài)、正常狀態(tài)和劃痕狀態(tài)的融合圖像的采集。輸送帶融合圖像采集完成后,為使得檢測效果更可靠,需對其進行預處理操作。論文分析了輸送帶縱向撕裂的特征,并據(jù)此對圖像進行了一系列的預處理,包括去除圖像噪聲,增強圖像對比度,突出檢測中感興趣的區(qū)域,提取出撕裂目標信息。預處理后的圖像視覺效果較好,撕裂目標較為突出,為后續(xù)縱向撕裂特征提取與識別做了較好準備。在得到的預處理圖像的基礎(chǔ)上,本文采用投影法提取出圖像的投影特征,并據(jù)此計算出圖像的幾何特征,即輸送帶縱向撕裂參數(shù):撕裂長度、寬度和面積。根據(jù)各類型輸送帶圖像投影特征的特點和縱向撕裂參數(shù)設(shè)定識別閾值并規(guī)定縱向撕裂的識別規(guī)則,最終將輸送帶圖像歸為:撕裂狀態(tài)、正常狀態(tài)、劃痕狀態(tài),實現(xiàn)不同狀態(tài)的輸送帶圖像的識別檢測。在MATLAB編程軟件平臺上對采集得到的輸送帶圖像進行預處理和撕裂特征提取與識別處理。實驗結(jié)果表明本文提出的輸送帶縱向撕裂一體化雙目視覺檢測方法能夠識別撕裂與劃痕,能夠預測潛在的撕裂,檢測精確度為96%以上,單幀圖像檢測時間小于21ms,是一種可靠的、實時的在線檢測方法。
[Abstract]:Conveyor is an important transportation tool in modern mining industry. In coal transportation, coal gangue, metal materials and other hard materials are often mixed in coal, which may lead to longitudinal tearing of conveyor belt. Such sudden accidents usually lead to the stoppage of mining equipment and production, resulting in huge economic losses. Therefore, we need real-time, reliable detection of belt longitudinal tear. In recent years, because machine vision can improve detection efficiency and precision, vision detection has become an important research direction in conveyor belt fault detection. Based on the complementary characteristics of machine vision, infrared imaging technology and visible light imaging technology, a binocular vision detection method for longitudinal tear of conveyor belt based on infrared and visible light fusion is proposed. According to the existing problems of the visual detection method of belt longitudinal tear, the design scheme of this method is put forward, which includes three parts: belt image collection, belt image preprocessing, belt image longitudinal tear feature extraction and recognition. Based on infrared and visible light fusion technology, a new integrated binocular vision sensor is proposed to collect infrared and visible light fusion images. The sensing device divides the incident coaxial light from the same lens into infrared light and visible light by means of prism, and enters into two photosensitive chips respectively, which can simultaneously capture infrared and visible light information in the same scene. There is no need for registration before image fusion. In this paper, the imaging principle of the sensor is studied, and its imaging process is simulated on MATLAB to verify its feasibility and effectiveness. Due to the theoretical simulation of the integrated binocular vision sensor proposed in this paper, the infrared image and the visible light image are collected by infrared camera and visible light camera respectively in the experiment part of the conveyor belt image acquisition. Then the infrared and visible image registration fusion, finally achieve the conveyor belt infrared and visible light fusion image acquisition. An image acquisition experiment platform is set up in the laboratory to realize the fusion image acquisition of conveyor belt in tearing state, normal state and scratch state. In order to make the detection more reliable, the belt fusion image acquisition needs to be preprocessed. In this paper, the characteristics of longitudinal tear of conveyor belt are analyzed, and a series of preprocessing is carried out on the image, including removing image noise, enhancing image contrast, highlighting the region of interest in detection, and extracting the information of tearing target. After preprocessing, the visual effect of the image is better, and the tear target is more prominent, which makes a good preparation for the subsequent longitudinal tear feature extraction and recognition. On the basis of the preprocessed images, the projection features of the images are extracted by projection method, and the geometric features of the images are calculated, that is, the longitudinal tearing parameters of the conveyor belt: tear length, width and area. According to the characteristics of projection features of each type of conveyor belt image and the parameters of longitudinal tear, the identification threshold is set and the recognition rule of longitudinal tear is stipulated. Finally, the conveyor belt image is classified as: tearing state, normal state, scratch state, and so on. Realization of different states of the conveyor belt image recognition and detection. The preprocessing, tearing feature extraction and recognition processing of the collected conveyor belt images are carried out on the platform of MATLAB programming software. The experimental results show that the proposed binocular visual detection method for longitudinal tear of conveyor belt can recognize tear and scratch, and can predict potential tear. The detection accuracy is over 96%, and the detection time of single frame image is less than 21ms. it is a reliable method. Real-time online detection method.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD528.1;TP391.41

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