本文選題：精密薄零件 + 邊緣檢測　； 參考：《電子科技大學(xué)》2016年碩士論文

【摘要】：精密薄零件是一類尺度較小,但在工業(yè)領(lǐng)域有重要應(yīng)用的零件。這類精密零件的邊緣形態(tài)對(duì)其工作特性有重要影響,因此此類精密薄零件的邊緣是檢測的重要指標(biāo)。但由于這類零件的尺度小,且邊緣薄,常規(guī)檢測手段難以進(jìn)行有效檢測。攝影測量作為一種現(xiàn)代工業(yè)檢測的重要手段,是解決這類精密零件檢測問題的有效方式。攝影測量的核心是數(shù)字圖像處理技術(shù),圖像處理技術(shù)在邊緣檢測領(lǐng)域已有諸多研究。但當(dāng)圖像檢測的精度要求與像素尺度數(shù)的量級(jí)相近時(shí),即使是單個(gè)像素的檢測差距都會(huì)對(duì)最終的檢測結(jié)果有較大的影響。而傳統(tǒng)的圖像處理算法在輸出最后結(jié)果時(shí)都會(huì)執(zhí)行“去模糊”操作,即將一個(gè)信息不確定的像素映射到一個(gè)非黑即白的二元集合,在小尺度下,圖像的邊緣信息往往是模糊的不確定的。這樣的去模糊操作會(huì)對(duì)精密薄零件的邊緣檢測造成精度的損失�；谶@個(gè)考慮,本文提出了基于模糊集的圖像邊緣檢測算法,與傳統(tǒng)方法不同的是,本文將模糊集內(nèi)的每個(gè)元素都可能是圖像的邊緣,引入全向梯度算子的概念,以全向梯度算子來衡量像素屬于圖像邊緣的可能性。同時(shí),不同于傳統(tǒng)方法執(zhí)行的去模糊操作,本文算法將模糊集中的模糊信息一直保留,最終得到的邊緣不是一個(gè)邊緣點(diǎn)集,而是一個(gè)邊緣模糊集。并且,為了減小模糊集內(nèi)元素的個(gè)數(shù),剔除明顯不合適的元素,提出基于像素信號(hào)能量的ROI提取算法:將像素塊的塊內(nèi)方差建模為信號(hào)能量,能量越大的區(qū)域其屬于邊緣的可能性越大,設(shè)定閾值,剔除那些在總的信號(hào)能量中占比極小的區(qū)域。在這兩步的基礎(chǔ)上,文章獨(dú)立推導(dǎo)了模糊集上的最小二乘圓心擬合算法,得到了模糊集下的圓心公式。由圓心公式可進(jìn)一步求得模糊集上各元素轉(zhuǎn)化到極坐標(biāo)下,并與CAD數(shù)據(jù)進(jìn)行對(duì)比,在一定顯著性水平下,即可找出誤差異常值完成對(duì)零件圖像的檢測。經(jīng)求解,可以正確地檢測出示例樣本中第69號(hào)齒尖(在極角347.3229,352.0326,348.7766,350.7192,351.3807,347.6453,347.3832)處存在明顯異常。經(jīng)人工復(fù)檢,本算法的漏檢率達(dá)0%,誤檢率為60%,可以有效的檢測出精密薄零件的異常點(diǎn)。
[Abstract]:Precision thin parts are a kind of small scale parts, but have important applications in the industrial field.The edge shape of this kind of precision part has an important influence on its working characteristics, so the edge of this kind of precision thin part is an important index of detection.However, because of its small scale and thin edge, conventional detection methods are difficult to detect effectively.As an important means of modern industrial detection, photogrammetry is an effective way to solve the problem of precision parts detection.The core of photogrammetry is digital image processing, which has been studied in the field of edge detection.However, when the accuracy of image detection is similar to the magnitude of pixel size, even the detection gap of a single pixel will have a great impact on the final detection results.Traditional image processing algorithms perform "de-blur" operations when they output final results, that is, a pixel with uncertain information is mapped to a non-black or white binary set, and at a small scale,The edge information of an image is often fuzzy and uncertain.Such a deblurring operation will result in a loss of accuracy in the edge detection of thin precision parts.Based on this consideration, an image edge detection algorithm based on fuzzy sets is proposed in this paper. Different from traditional methods, every element in a fuzzy set is likely to be the edge of an image, and the concept of omnidirectional gradient operator is introduced in this paper.The omnidirectional gradient operator is used to measure the possibility that pixels belong to the edge of the image.At the same time, different from the traditional de-fuzzy operation, the fuzzy information in the fuzzy set is always preserved by the algorithm, and the resulting edge is not a set of edge points, but a set of edge fuzzy.Furthermore, in order to reduce the number of elements in the fuzzy set and eliminate the obviously inappropriate elements, a ROI extraction algorithm based on pixel signal energy is proposed: the intra-block variance of the pixel block is modeled as the signal energy.The larger the energy is, the more likely it is to belong to the edge. The threshold is set to eliminate the regions that account for a very small proportion of the total signal energy.On the basis of these two steps, the least square centroid fitting algorithm on fuzzy sets is derived independently, and the center formula for fuzzy sets is obtained.The elements in the fuzzy set can be further transformed to polar coordinates by the center of circle formula, and compared with the CAD data. At a certain level of significance, the abnormal value of the error can be found to complete the detection of the part image.After solving, we can correctly detect the obvious anomaly at the tip of tooth No. 69 in the sample (347.3229352.0326) at the polar angle 348.7766350.7192v 351.3807347.6453n 347.3832.After manual rechecking, the missed detection rate of this algorithm is 0 and the false detection rate is 60. It can effectively detect the abnormal points of the precision thin parts.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH71;TP391.41

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