Detection and classification of surface defects of gun barrels using computer vision and machine learning

Shanmugamani, Rajalingappaa; Sadique, Mohammad; Ramamoorthy, B.

doi:10.1016/j.measurement.2014.10.009

Cited by 109 publications

(50 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover they integrated different shape signatures like centroid distance, area function, angular function, complex coordinates, polar coordinates, and angular radial function for experimentation. The surface defects found in gun barrels are identified and classified using texture features such as energy, uniformity, correlation, contrast, and entropy (Rajalingappaa Shanmugamani et al, 2014). They have used Sequential Forward Selection (SFS) algorithm for selection of best features set.…”

Section: Multi -Feature Based Descriptorsmentioning

confidence: 99%

Machine parts recognition and defect detection in automated assembly systems using computer vision techniques

2016

Rev. Téc. Ing. Univ. Zulia.

View full text Add to dashboard Cite

Assembly line automation gets more significance to cope up with increasing needs of latest technology machines which are used in industry and society. This paper presents a computationally efficient 2D computer vision based approach to recognize the machine parts and detect damaged parts on the assembly line. The image acquisition system which is part of the assembly line setup acquires data from the moving machine parts in line. Captured machine part image data undergoes image preprocessing techniques like background subtraction, binarization, scaling, and noise and holes removal to transform the data suitable for further processing. Then a contour of the machine parts are extracted and normalized by equal part area method to describe the shape. It gives important clues for machine part shape recognition and defect identification. For experimental purpose a model shape for each machine part is developed, the shape recognition and defect detection are performed with only reference to the model shape. The defects in the machine part such as damage, cracks are identified by the similarity measure between model shape and the data extracted from machine part of the assembly line. The detection and identification of defects at the early stage will helps smooth production process, saves production cost and time.

show abstract

Section: Multi -Feature Based Descriptorsmentioning

confidence: 99%

Machine parts recognition and defect detection in automated assembly systems using computer vision techniques

2016

Rev. Téc. Ing. Univ. Zulia.

View full text Add to dashboard Cite

show abstract

“…In the field of quality control and inspection of a sensory workpiece, it is very hard to make the machine's perception close to humans, and a miniature stepper motor module for smart devices needs precise control [2]. With the development of neural networks, more deep learning models have become outstanding in the field of detection, including machine vision non-standard workpiece detection [3][4][5][6], motor fault current signal detection [7], bearing vibration signal detection [8], and gear fault diagnosis based on sound signals [9]. Due to extensive use of convolutional neural networks and activation function, the method of analyzing high-dimensional features of objects by non-linear mapping will bring great improvement to pattern recognition [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

et al. 2019

View full text Add to dashboard Cite

It is difficult to combine human sensory cognition with quality detection to form a pattern recognition system based on human perception. In the future, miniature stepper motor modules will be widely used in advanced intelligent equipment. However, the reducer module based on powder metallurgy parts and the stepper motor may have various defects during operation, with varying definitions of those that affect the user comfort. It is tremendously important to develop an intelligent system to effectively simulate human senses. In this work, an elaborated personification of the perceptual system is proposed to simulate the ventral and flow of the human perception system: two branch systems consisting of a spatiotemporal convolutional neural network (S-CNN) and a concatenated HoppingNet temporal convolutional neural network (T-CNN). To ensure high robustness of the system, we combined principal component analysis (PCA) with the opinions of an experienced quality control (QC) team members to screen the data, and used a bionic ear to simulate human perception characteristics. After repeated comparisons of the tester, the results show that our anthropoid pattern sensing system has high accuracy and robustness for a stepper motor module.

show abstract

“…Ideally, one would like to detect and classify defects only from one single RGB image. However, this is not often an easy task especially when dealing with complex defects [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks

Essid¹,

Laga²,

Samir³

2018

PLoS ONE

View full text Add to dashboard Cite

This paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by using autoencoder deep neural network (DNN) architecture, we are able to not only classify manufacturing defects, but also localize them with high accuracy. Compared to traditional techniques, DNNs are able to learn, in a supervised manner, the visual features that achieve the best performance. Our experiments on a database of real images demonstrate that our approach overcomes the state-of-the-art while remaining computationally competitive.

show abstract

Detection and classification of surface defects of gun barrels using computer vision and machine learning

Cited by 109 publications

References 19 publications

Machine parts recognition and defect detection in automated assembly systems using computer vision techniques

Machine parts recognition and defect detection in automated assembly systems using computer vision techniques

Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks

Contact Info

Product

Resources

About