針對鋸片缺陷檢測的機器視覺系統(tǒng)設(shè)計

摘要：機器視覺檢測技術(shù)具有非接觸、高效、準確等優(yōu)點，它符合現(xiàn)代工業(yè)生產(chǎn)的發(fā)展需要，本文相對于傳統(tǒng)方法提出了針對鋸片缺陷的機器視覺檢測技術(shù)，并在現(xiàn)有的實驗條件下進行研究和分析。首先闡述了此課題的背景和意義以及機器視覺技術(shù)在國內(nèi)外的發(fā)展和應用現(xiàn)狀，介紹了機器視覺檢測系統(tǒng)的各硬件和軟件的主要構(gòu)成部分及其作用，系統(tǒng)主要通過鏡頭、攝像機，圖像采集卡等完成圖像的采集，再借助比較成熟的圖像處理算法，包括亞像素精度閥值分割、幾何基元的分割、輪廓特征提取、線段擬合等，以Halcon為實驗軟件平臺實現(xiàn)圖像的處理并輸出結(jié)果。通過軟硬件的合理搭配完成對鋸片缺陷檢測的機器視覺系統(tǒng)設(shè)計，提高工業(yè)生產(chǎn)檢測效率。

關(guān)鍵詞：機器視覺； 圖像處理； 鋸片檢測； HALCON

Design of Saw Blades Defects Inspecting System
Based on Machine Vision
Abstract: Machine vision inspection technology with non-contact, efficient and accurate etc., which accords with modern industrial production development needs, this paper puts forward relative to traditional methods of saw blades defects machine vision of testing technology, and the experimental conditions in the existing researches and analysis. First expounded the background and meaning of the subject machine vision technology at home and abroad and the development and application of machine vision introduced the present situation and the testing system of hardware and software of the main components of and function, system mainly through the lens, camera, image acquisition card etc complete image collection, again with comparatively mature image processing algorithms, including subpixel-precision thresholding, segmentation of contours into lines and circles, contour features extraction, fitting lines etc, in order to realize the software platform for experimental Halcon of image processing and output. Through the software and hardware of the reasonable collocation completed the blade defects detection machine vision system design, improve industrial production and testing efficiency.

Key words: Machine Vision, Image Processing, Inspection of Saw Blades, Halcon
 

目    錄
第1章  緒  論 1
1.1  課題背景及研究意義 1
1.1.1  課題背景 1
1.1.2  課題研究意義 1
1.2  機器視覺概述 1
1.3  機器視覺的國內(nèi)外發(fā)展現(xiàn)狀 2
1.4  本文主要研究內(nèi)容 3
第2章  鋸片缺陷檢測系統(tǒng)的硬件設(shè)計 4
2.1  引言 4
2.2  檢測原理 4
2.3  采集方案設(shè)計 5
2.3.1  光源的選擇 5
2.3.2  照明方式的選擇 6
2.3.3  照明環(huán)境的選擇 10
2.3.4  相機的選擇 11
2.3.5  鏡頭的選擇 12
2.3.6  圖像采集卡的選擇 12
2.4  本章小結(jié) 14
第3章  軟件算法研究及實驗設(shè)計 15
3.1  機器視覺軟件HALCON 15
3.1.1  HALCON概述 15
3.1.2  HALCON的體系和數(shù)據(jù)結(jié)構(gòu) 16
3.2  圖像處理算法 17
3.2.1  圖像分割 17
3.2.2  特征提取 21
3.2.3  幾何基元的分割和擬合 24
3.3  實驗設(shè)計 28
3.3.1  鋸片檢測算法流程 28
3.3.2  算法編程實現(xiàn) 28
3.3.3  實驗結(jié)果分析 31
3.4  本章小結(jié) 32
結(jié)論與展望 33
致謝 34
參考文獻 35
附錄 36