کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7124766 | 1461527 | 2015 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Detection and classification of surface defects of gun barrels using computer vision and machine learning
ترجمه فارسی عنوان
تشخیص و طبقه بندی نقص سطحی بشکهای تفنگ با استفاده از چشم انداز کامپیوتری و یادگیری ماشین
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کلمات کلیدی
بینایی ماشین، تشخیص نقص بشکه تفنگ، طبقه بندی، فضای مقیاس،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
This work proposes a machine vision based approach for the detection and classification of the surface defects such as normal wear, corrosive pitting, rust and erosion that are usually present in used gun barrels. Surface images containing the defective regions of several used gun barrels were captured in a non-destructive manner using a Charge-Coupled Device (CCD) camera attached with a miniature microscopic probe. Among the captured images, normal wear appeared as bright and the rest of the three defects appeared as dark. Therefore, the classification has been carried out in two stages. Various segmentation methods were tested and extended maxima transform gave the best result. The defective area was calculated in metric units. Multiple textural features based on histogram and gray level co-occurrence matrix were extracted from the segmented images and ranked them automatically using the sequential forward feature selection method in order to select the best minimal features for the classification purpose. Many classifiers based on Bayes, k-Nearest Neighbor, Artificial Neural Network and Support Vector Machine (SVM) were tested and the results demonstrated the efficacy of SVM for this application. All these steps were carried out at six different scales of image sizes and the best scale was selected for the entire analysis based on the segmentation and classification accuracy. The introduction of this Gaussian scale spacing concept could reduce the computation without compromising on the accuracy. Overall, the methodology forms a novel framework for surface defect detection and classification that has a potential to automate the inspection process.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 60, January 2015, Pages 222-230
Journal: Measurement - Volume 60, January 2015, Pages 222-230
نویسندگان
Rajalingappaa Shanmugamani, Mohammad Sadique, B. Ramamoorthy,