本文選題：復(fù)合絕緣子芯棒 + 缺陷分割　； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：基于視覺的缺陷檢測技術(shù)以其高準(zhǔn)確度、高效率等優(yōu)勢在工業(yè)檢測領(lǐng)域被廣泛應(yīng)用。復(fù)合絕緣子芯棒質(zhì)量是影響絕緣子質(zhì)量的重要因素,芯棒缺陷的檢測比較困難。傳統(tǒng)人工使用肉眼進(jìn)行檢測的方法存在可靠性差,效率低下的等缺陷。將基于機(jī)器視覺的缺陷檢測技術(shù)應(yīng)用于絕緣子芯棒自動(dòng)缺陷檢測與分類,對(duì)于提高芯棒質(zhì)量檢測的效率和可靠性具有重要意義。論文研究基于視覺的芯棒缺陷檢測與分類算法并研制芯棒缺陷檢測軟件。論文針對(duì)芯棒的特點(diǎn)展開一系列算法研究。論文的主要工作如下:(1)針對(duì)芯棒缺陷的特點(diǎn),研究了基于視覺顯著性模型的缺陷目標(biāo)定位與分割算法,取得了良好的缺陷定位與分割效果。(2)基于不同的芯棒樣本,對(duì)芯棒缺陷區(qū)域的特征進(jìn)行了分析,研究能夠標(biāo)識(shí)芯棒缺陷的特征,包括缺陷區(qū)域的長寬比、方向、形心位置、矩形度、致密度、平均灰度值、灰度均方差。(3)基于BP神經(jīng)網(wǎng)絡(luò)對(duì)缺陷圖像進(jìn)行分類。分析并確定了神經(jīng)網(wǎng)絡(luò)模型參數(shù)的選擇和訓(xùn)練過程。(4)設(shè)計(jì)了芯棒缺陷檢測軟件系統(tǒng)的方案,并完成了缺陷檢測軟件系統(tǒng)的開發(fā)。
[Abstract]:Vision-based defect detection technology is widely used in the field of industrial detection because of its high accuracy and high efficiency. The quality of composite insulator mandrel is an important factor affecting the quality of insulator, and the detection of mandrel defects is difficult. The traditional manual inspection method with naked eye has some defects, such as low reliability and low efficiency. The application of machine vision based defect detection technology to the automatic defect detection and classification of insulator mandrel is of great significance for improving the efficiency and reliability of mandrel quality detection. In this paper, visual-based mandrel defect detection and classification algorithms are studied and a mandrel defect detection software is developed. In this paper, a series of algorithms are studied according to the characteristics of mandrel. The main work of this paper is as follows: (1) aiming at the characteristics of mandrel defects, a defect target localization and segmentation algorithm based on visual salience model is studied. A good defect localization and segmentation effect is obtained. The characteristics of mandrel defect region are analyzed, and the characteristics that can identify the mandrel defect are studied, including the aspect ratio, direction, centroid position, rectangle degree, density, average gray value, etc. Based on BP neural network, defect images are classified. The selection and training process of neural network model parameters are analyzed and determined. The scheme of mandrel defect detection software system is designed, and the development of defect detection software system is completed.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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本文編號(hào)：1968446