電腦視覺結合SCARA應用於驗孕片外殼之瑕疵檢測

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Electrical Engineering > Electronic Thesis & Dissertation > Item 987654321/81448

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81448

Title:	電腦視覺結合SCARA應用於驗孕片外殼之瑕疵檢測
Authors:	王志恩;Wang, Zhi-En
Contributors:	電機工程學系
Keywords:	電腦視覺;影像辨識;瑕疵偵測;射出成型;SCARA;機械手臂;Computer vision;Image recognition;defect detection;Injection molding;SCARA;Robotic arm
Date:	2019-07-25
Issue Date:	2019-09-03 15:54:17 (UTC+8)
Publisher:	國立中央大學
Abstract:	本論文研究目的為使用電腦視覺對射出成型(Injection Moulding)的驗孕片外殼進行自動化瑕疵偵測，並結合SCARA機械手臂吸取輸送帶上移動的驗孕片外殼進行瑕疵篩選。本論文實驗中，驗孕片外殼由小型輸送帶運送模擬現實工廠的流水線環境，其中輸送帶被設計成前後高低兩段，兩段中間放置自行印製的3D列印斜面擋板模型，藉此達到驗孕片外殼掉落時自動翻面的機制。兩台工業攝影機分別拍攝驗孕片外殼的正反面影像進行瑕疵檢測，本實驗中需檢測的四種瑕疵分別為：油漬、黑線黑點、裁剪殘料、缺料。為去除驗孕片外殼以外的環境背景，使用影像二值化與連通物件法對影像進行前處理，而對每種瑕疵分別採用不同的演算法進行分析與偵測，其中利用了HSV色彩空間分離油漬與驗孕片外殼背景以偵測油漬瑕疵；使用了Neighboring Difference Filter (NDF)圖像濾波器，有效的抓取模糊表面上的瑕疵，以偵測黑線黑點瑕疵；使用道格拉斯-普克近似輪廓演算法判斷邊緣是否有殘料殘留，以偵測裁剪殘料瑕疵；先透過驗孕片外殼面積比例分析是否為本體缺料，再於HSV色彩空間偵測接腳部分分析是否為斷腳缺料, 以偵測缺料瑕疵。檢測後，若驗孕面外殼任一面被檢測出瑕疵存在，則命令SCARA機械手臂吸取驗孕片外殼進行瑕疵篩選。最後將討論論文實驗中四種瑕疵偵測方法於不同參數下的偵測結果與準確率。;The main purpose of this thesis is to use computer vision to automatically detect the shell defects on the pregnancy test kit, and use SCARA robot to pick up and exclude the defective products of them on the moving conveyor belt for realizing the filtering function. In this study, the shell of the pregnancy test kit is transported by a small conveyor belt, to simulate a real factory assembly line. The conveyor belt is designed as two sections: the front section is higher and the rear section is lower. Then when the shell of the pregnancy test kit is transported from high section and drops to the low section, at the same time, it will turn over from one side to the reverse side because of a baffle made by 3D printer at the junction of two sections. Two industrial cameras take the photo of the shell at the high and low sections respectively. Therefore, four kinds of defects need to be detected, which are black spots and lines, oil stains, cutting residual materials, and lack of materials. In order to remove the background outside the shell, the image is pre-processed with binarization and connected-component labeling. It is noted that different defects are analyzed and detected by different algorithms, respectively. For instance, the oil stain and the background can be separated in HSV color space. Black spots and lines are detected by the Neighboring Difference Filter, which can effectively detect defects on blurred surfaces. The cutting residual on the edge is detected by the Douglas-Puck contour algorithm. In the case of the lack of materials, the proportion of the area of the pregnancy test kit is firstly analyzed to determine whether the body is short of material, and then analyzed whether the color of the pin in the HSV color space is broken. If defects are detected on any surfaces, the image taken by the industrial camera on the low section of the conveyor belt will be applied to calculate its moving speed and position coordinate X and Y at the current time. After then, the SCARA robot arm is commanded to pick the shell up if any defect is found. Finally, there are some experiments to show the proposed algorithms are effective for the defects detection of shells and the accurate operations of the SCARA robot.
Appears in Collections:	[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

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