本文關(guān)鍵詞： 計(jì)算機(jī)視覺(jué) 缺陷檢測(cè) 特征提取 實(shí)時(shí)檢測(cè)　出處：《遼寧科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：鎂是最輕的結(jié)構(gòu)金屬材料之一,因其質(zhì)量輕、剛性好、密度低、散熱快等優(yōu)點(diǎn)被廣泛應(yīng)用于航空、運(yùn)輸、化工等各個(gè)領(lǐng)域。在鎂合金薄板的軋制過(guò)程中,因機(jī)械及控制精度原因,易出現(xiàn)邊裂、波紋、褶皺等缺陷,如果這個(gè)問(wèn)題不能夠被及時(shí)解決,將嚴(yán)重影響鎂合金薄板成品質(zhì)量,因此如何對(duì)鎂合金薄板表面缺陷進(jìn)行高效、快速地檢測(cè)將成為鎂合金薄板制造業(yè)的關(guān)鍵。傳統(tǒng)的人工檢測(cè)方法,效率低下,誤檢率高,而且耗費(fèi)了大量的人力資源�，F(xiàn)有的檢測(cè)技術(shù)中常用的渦流檢測(cè)技術(shù)、紅外檢測(cè)技術(shù)以及漏磁檢測(cè)技術(shù)等由于其原理的局限性,檢測(cè)速率和可識(shí)別的缺陷類型極為有限。近年來(lái),隨著計(jì)算機(jī)、自動(dòng)化、人工智能、圖像識(shí)別等技術(shù)的發(fā)展,以計(jì)算機(jī)視覺(jué)為核心的表面缺陷檢測(cè)技術(shù)已經(jīng)成為鎂合金薄板生產(chǎn)的研究重點(diǎn)。本課題研制了鎂合金薄板表面缺陷識(shí)別系統(tǒng),其實(shí)時(shí)采集薄板表面圖像,通過(guò)計(jì)算機(jī)視覺(jué)技術(shù)自動(dòng)檢測(cè)缺陷并利用貝葉斯分類器判定缺陷類型,為后續(xù)實(shí)現(xiàn)生產(chǎn)過(guò)程的無(wú)人值守及高度自動(dòng)化控制奠定了基礎(chǔ)。主要完成工作如下:(1)根據(jù)實(shí)驗(yàn)環(huán)境、生產(chǎn)線上的實(shí)際狀況以及經(jīng)濟(jì)預(yù)算,確定了本系統(tǒng)的軟硬件設(shè)計(jì)方案,如選擇LED光源、確定照明方案、配置操作系統(tǒng)等。(2)由于薄板帶表面具有對(duì)比度低、易發(fā)生光照反射的特點(diǎn),選擇了直方圖均衡化、中值濾波等圖像預(yù)處理的方法。(3)通過(guò)研究分析薄板常見(jiàn)的五個(gè)缺陷特征,選擇了幾何特征、紋理特征中的九個(gè)特征值組成特征向量。(4)在詳實(shí)對(duì)比了各種缺陷分類器特性的基礎(chǔ)上,結(jié)合實(shí)際生產(chǎn)中對(duì)于檢測(cè)速度的要求,最終確定了具有快速處理多類問(wèn)題優(yōu)勢(shì)的貝葉斯分類器來(lái)判斷缺陷分屬問(wèn)題的劃分。(5)通過(guò)研究各種軟件平臺(tái),最終實(shí)現(xiàn)了薄板表面缺陷實(shí)時(shí)檢測(cè)系統(tǒng)。本系統(tǒng)在實(shí)時(shí)檢測(cè)過(guò)程中,識(shí)別率為83.6%,平均識(shí)別1個(gè)樣本的時(shí)間為16毫秒,滿足實(shí)際工業(yè)化生產(chǎn)需要。
[Abstract]:Magnesium is one of the lightest structural metal materials, because of its advantages of light weight, good rigidity, low density, fast heat dissipation and so on, it is widely used in aviation, transportation, chemical industry and other fields. Because of mechanical and control precision, defects such as edge crack, ripple, fold and so on are easy to appear. If this problem can not be solved in time, it will seriously affect the quality of magnesium alloy sheet, so how to carry on the high efficiency to the magnesium alloy sheet surface defect, Rapid detection will be the key to the manufacturing of magnesium alloy sheet. The traditional manual detection method has low efficiency, high error detection rate, and consumes a lot of human resources. Due to the limitation of the principle of infrared detection and magnetic flux leakage detection, the detection rate and identifiable defect types are very limited. In recent years, with the development of computer, automation, artificial intelligence, image recognition and other technologies, The surface defect detection technology with computer vision as the core has become the research focus of magnesium alloy sheet production. In this paper, the surface defect recognition system of magnesium alloy sheet is developed, which can collect the surface image of magnesium alloy sheet in real time. Automatic detection of defects by computer vision and identification of defect types by Bayesian classifier lay the foundation for unattended and highly automated control of the subsequent production process. The main work accomplished is as follows: 1) according to the experimental environment, The actual situation and economic budget of the production line, the hardware and software design scheme of the system is determined, such as selecting LED light source, determining lighting scheme, configuring operating system, etc.) because of the low contrast on the surface of the thin strip, The method of image preprocessing, such as histogram equalization, median filter and so on, is chosen. By studying and analyzing the five common defect features of thin plate, the geometric features are selected. Based on the detailed comparison of the characteristics of various defect classifiers, combined with the requirements of detection speed in actual production, Finally, the Bayesian classifier which has the advantage of fast processing multi-class problems is determined to judge the partition of defect problem. In the process of real-time detection, the recognition rate of the system is 83.6, and the average time of identifying one sample is 16 milliseconds, which meets the needs of practical industrial production.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TG115.28;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

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本文編號(hào)：1528792