Detection and classification of surface defects of gun barrels using computer vision and machine learning. (January 2015)

• Record Type: Journal Article
• Authors: Shanmugamani, Rajalingappaa; Sadique, Mohammad; Ramamoorthy, B.
• Is Part Of: Measurement. Volume 60(2015:Feb.)
• Page Start: 222
• Page End: 230
• Publication Date: 2015-01
• Subjects: Machine vision -- Gun barrel defect detection -- Classification -- Scale space; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2014.10.009
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Framework for gun barrel surface defect detection and classification is developed. Multiple segmentation techniques for defect area evaluation are tested. Numerous machine learning methods for classifying the type of defects are compared. A multi-level Gaussian Scale space is implemented to reduce processing time. Abstract: 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.