基于機器視覺的日化用品泵頭缺陷高速在線檢測系統(tǒng)研究
發(fā)布時間：2018-07-22 16:19
【摘要】：機器視覺技術從發(fā)展初期開始便被廣泛應用于各個領域,關鍵在于其速度快、信息量大、功能多等特點。其中,應用最多的是工業(yè)生產線上的產品自動化檢測領域。采用機器視覺檢測系統(tǒng)可以提高檢測速度、檢測效率,降低工業(yè)生產成本,因此近年來對機器視覺技術的研究很多都集中在工業(yè)檢測領域上。本文根據項目要求,研究了基于機器視覺技術的日化用品泵頭缺陷檢測系統(tǒng)以及一種新型的ROI區(qū)域仿射變換參量的快速獲取算法。本文主要研究內容如下:在本文中,第一章綜述了本課題的背景、機器視覺技術的發(fā)展狀況、國內外研究現(xiàn)狀、以及研究內容和研究意義。第二章研究了系統(tǒng)需求、總體設計方案以及各功能模塊。本章中詳細研究機器視覺硬件系統(tǒng)部分內容,機器視覺技術研發(fā)離不開相機光源等硬件,并且硬件的選型和光路的設計直接決定著系統(tǒng)的性能以及可靠性,所以還對相機、鏡頭、光源等各型號以及其適用場合作了詳細研究。特別是光源部分,作為檢測系統(tǒng)中最為重要的部分,合適的光源以及光路系統(tǒng)直接影響拍攝圖像的質量和后期圖像處理的難度。所以選擇合適的光源跟光路系統(tǒng)可以降低后期圖像處理的難度從而增加系統(tǒng)的性能和穩(wěn)定性。本章還簡要研究了眩光如何消除以及眩光的消除原理。第三章主要研究角點檢測算法中三種常用的角點檢測算子,并深入研究了它們的原理。我們通過對比分析Moravec算子、Harris算子以及SUSAN算子,發(fā)現(xiàn)Harris算子對本項目中的待測物進行角點檢測能達到最優(yōu)的效果,所以最后確定使用Harris算子作為本項目圖像處理算法的角點檢測算法。第四章主要研究角點匹配算法,簡要研究角點匹配算法的四要素,并詳細研究本人自己推導的一組最優(yōu)仿射變換參量計算公式(一種新型的ROI區(qū)域仿射變換參量的快速獲取算法)、關鍵推導過程及實驗驗證。通過使用該組公式實現(xiàn)本項目圖像處理算法中的角點匹配算法部分,直接快速求出角點匹配所需的平移旋轉參量。經實驗驗證該公式能夠非常完美配合系統(tǒng)中的圖像處理算法。針對目前研發(fā)機器視覺檢測系統(tǒng)需要對特定需求開發(fā)特定系統(tǒng),本文第五章研究了基于VC6.0平臺的MFC+halcon軟件開發(fā)的機器視覺軟件系統(tǒng),具體為軟件系統(tǒng)的整體結構設計以及功能模塊設計。整體結構設計中對相機的初始化、調用回調函數(shù)、設置啟動定時器、開辟多線程、控制電磁閥等進行了研究。各個功能模塊部分主要包括相機控制模塊、圖像采集模塊、圖像處理模塊、串口通訊、光源控制模塊等,在這部分還研究了本系統(tǒng)所用的一部分圖像處理算法,圖像處理算法在機器視覺檢測系統(tǒng)中是最為重要的,也是本項目最為核心的一部分。本研究設計的日化用品泵頭缺陷檢測系統(tǒng)經過大量的實驗測試表明,該系統(tǒng)能對日化用品泵頭表面缺陷能準確檢測出來,有效實現(xiàn)了日化用品泵頭表面缺陷的實時非接觸檢測。
[Abstract]:The machine vision technology has been widely used in various fields since its early development. The key lies in its fast speed, large amount of information and many functions. Among them, the application of the machine vision is the most widely used in the field of automatic inspection of the products on the industrial production line. The use of machine vision detection system can improve the speed of detection, detection efficiency and reduce the cost of industrial production. So many research on machine vision technology in recent years are concentrated in the field of industrial testing. In this paper, based on the requirements of the project, this paper studies the pump head defect detection system based on machine vision technology and a new fast acquisition algorithm for the ROI regional affine transformation parameters. The main contents of this paper are as follows: in this paper, The chapter summarizes the background of this topic, the development of machine vision technology, the current research status at home and abroad, and the research content and research significance. The second chapter studies the system requirements, the overall design scheme and the functional modules. In this chapter, the part of the machine vision hardware system is studied in detail, and the research of machine vision technology can not be separated from the camera light source. Such as hardware, and the selection of hardware and the design of optical path directly determines the performance and reliability of the system, so the cooperation of the camera, lens, light source and other models, as well as its applicable field, is also studied in detail. Especially the light source part, as the most important part of the detection system, the appropriate light source and optical path system directly affect the picture. The selection of the appropriate light source and optical path system can reduce the difficulty of the later image processing and increase the performance and stability of the system. In this chapter, the principle of how to eliminate glare and the elimination of glare is briefly studied. The third chapter mainly studies the three common corner points in the corner detection algorithm. By comparing and analyzing the Moravec operator, the Harris operator and the SUSAN operator, we find that the Harris operator can detect the corner point of the item in this project to achieve the best effect. Finally, the Harris operator is used as the corner detection algorithm for the image processing algorithm of this project. The four chapter mainly studies the corner matching algorithm, briefly studies the four elements of the corner matching algorithm, and studies in detail a set of optimal affine transformation parameters calculation formula derived by myself (a new fast acquisition algorithm for the ROI area affine transformation parameter), the key derivation process and experimental verification. By using this set of formulas to realize the project The corner matching algorithm part of the image processing algorithm is used to quickly find the translational rotation parameters needed for the corner matching. It is proved that the formula can perfectly match the image processing algorithms in the system. The fifth chapter of this paper is based on the study of V based on the specific requirements of the machine vision detection system. The machine vision software system developed by MFC+halcon software of C6.0 platform is specifically designed for the overall structure design and function module of the software system. The initialization of the camera, the call back function, the setting of the start timer, the opening of the multi thread and the control of the solenoid valve are studied in the whole structure design. It includes the camera control module, the image acquisition module, the image processing module, the serial port communication and the light source control module. In this part, a part of the image processing algorithm used in this system is also studied. The image processing algorithm is the most important part of the machine vision detection system, and it is also the most important part of this project. The test system of the product pump head defect test shows that the system can accurately detect the surface defects of the pump head of the daily products and effectively realize the real-time non-contact detection of the surface defects of the daily product pump head.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2138034
[Abstract]:The machine vision technology has been widely used in various fields since its early development. The key lies in its fast speed, large amount of information and many functions. Among them, the application of the machine vision is the most widely used in the field of automatic inspection of the products on the industrial production line. The use of machine vision detection system can improve the speed of detection, detection efficiency and reduce the cost of industrial production. So many research on machine vision technology in recent years are concentrated in the field of industrial testing. In this paper, based on the requirements of the project, this paper studies the pump head defect detection system based on machine vision technology and a new fast acquisition algorithm for the ROI regional affine transformation parameters. The main contents of this paper are as follows: in this paper, The chapter summarizes the background of this topic, the development of machine vision technology, the current research status at home and abroad, and the research content and research significance. The second chapter studies the system requirements, the overall design scheme and the functional modules. In this chapter, the part of the machine vision hardware system is studied in detail, and the research of machine vision technology can not be separated from the camera light source. Such as hardware, and the selection of hardware and the design of optical path directly determines the performance and reliability of the system, so the cooperation of the camera, lens, light source and other models, as well as its applicable field, is also studied in detail. Especially the light source part, as the most important part of the detection system, the appropriate light source and optical path system directly affect the picture. The selection of the appropriate light source and optical path system can reduce the difficulty of the later image processing and increase the performance and stability of the system. In this chapter, the principle of how to eliminate glare and the elimination of glare is briefly studied. The third chapter mainly studies the three common corner points in the corner detection algorithm. By comparing and analyzing the Moravec operator, the Harris operator and the SUSAN operator, we find that the Harris operator can detect the corner point of the item in this project to achieve the best effect. Finally, the Harris operator is used as the corner detection algorithm for the image processing algorithm of this project. The four chapter mainly studies the corner matching algorithm, briefly studies the four elements of the corner matching algorithm, and studies in detail a set of optimal affine transformation parameters calculation formula derived by myself (a new fast acquisition algorithm for the ROI area affine transformation parameter), the key derivation process and experimental verification. By using this set of formulas to realize the project The corner matching algorithm part of the image processing algorithm is used to quickly find the translational rotation parameters needed for the corner matching. It is proved that the formula can perfectly match the image processing algorithms in the system. The fifth chapter of this paper is based on the study of V based on the specific requirements of the machine vision detection system. The machine vision software system developed by MFC+halcon software of C6.0 platform is specifically designed for the overall structure design and function module of the software system. The initialization of the camera, the call back function, the setting of the start timer, the opening of the multi thread and the control of the solenoid valve are studied in the whole structure design. It includes the camera control module, the image acquisition module, the image processing module, the serial port communication and the light source control module. In this part, a part of the image processing algorithm used in this system is also studied. The image processing algorithm is the most important part of the machine vision detection system, and it is also the most important part of this project. The test system of the product pump head defect test shows that the system can accurately detect the surface defects of the pump head of the daily products and effectively realize the real-time non-contact detection of the surface defects of the daily product pump head.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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