發(fā)布時間：2018-04-02 10:20

  本文選題：圖像特征　切入點：鋼軌缺陷檢測　出處：《西南科技大學》2017年碩士論文

【摘要】：針對現(xiàn)有鋼軌表面缺陷檢測方法存在適應性差、可靠性不強、自動化程度不高等問題,設(shè)計了一種基于圖像特征的鋼軌表面缺陷檢測系統(tǒng),使用數(shù)字圖像處理技術(shù)與機器學習方法對鋼軌表面幾種典型缺陷進行判斷識別。本文首先介紹了無損檢測技術(shù)的發(fā)展現(xiàn)狀,并分析了鋼軌表面幾種典型缺陷類型以及產(chǎn)生的原因,設(shè)計了一種基于圖像特征的鋼軌表面缺陷檢測系統(tǒng),該系統(tǒng)主要包括圖像預處理、特征描述、分類器設(shè)計等3個方面。在預處理階段,首先通過改進投影法提取出鋼軌所在區(qū)域;其次,通過對噪聲類型進行分析,選擇使用自適應中值濾波算法對鋼軌圖像進行濾波操作;針對鋼軌表面圖像灰度分布均勻的特點,提出一種分塊自適應模糊增強算法,根據(jù)子塊熵值判斷,對缺陷潛在子塊進行模糊增強,并通過OSTU閾值分割方法實現(xiàn)圖像分割;使用空頻域相結(jié)合的方法,分別提取缺陷圖像的灰度、幾何、不變矩,以及小波變換后各區(qū)域的均值、方差作為鋼軌圖像的特征;最后,通過設(shè)計訓練生成BP神經(jīng)網(wǎng)絡(luò)模型,來達到鋼軌圖像表面缺陷檢測的目的。通過實驗結(jié)果分析,該系統(tǒng)可以實現(xiàn)裂紋、劃傷、軋疤、凹坑等4種典型缺陷的識別與分類,總體達到漏檢率8%,準確率88.5%的指標,實現(xiàn)表明該系統(tǒng)對實際應用具有一定參考價值。
[Abstract]:Aiming at the problems of poor adaptability, low reliability and low automation in existing rail surface defect detection methods, a rail surface defect detection system based on image features is designed. Several typical defects on rail surface are judged and identified by digital image processing technology and machine learning method. This paper first introduces the development status of nondestructive testing technology. Several typical defect types on rail surface and their causes are analyzed. A rail surface defect detection system based on image features is designed. The system mainly includes image preprocessing and feature description. In the preprocessing stage, the rail region is extracted by the improved projection method, and the noise type is analyzed, and the adaptive median filter algorithm is used to filter the rail image. According to the characteristic of uniform gray distribution of rail image, a block adaptive fuzzy enhancement algorithm is proposed. According to the entropy value of sub-block, the defect potential sub-block is enhanced by fuzzy enhancement, and the image segmentation is realized by OSTU threshold segmentation method. The space-frequency domain method is used to extract the gray level, geometry, invariant moment of defect image, and the mean value and variance of each region after wavelet transform as the features of rail image. Finally, BP neural network model is generated by design training. Through the analysis of the experimental results, the system can recognize and classify four typical defects, such as crack, scratch, rolling scar and pit, and reach the target of 8% leakage rate and 88.5% accuracy. The implementation shows that the system has certain reference value for practical application.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U216.3;TP391.41

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本文編號：1699938