Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach

2020 ◽  
Author(s):  
Malathy Jawahar ◽  
N. K. Chandra Babu ◽  
K. Vani ◽  
L. Jani Anbarasi ◽  
S. Geetha
Keyword(s):  
Particle Swarm Optimization ◽  
Defect Detection ◽  
Surface Defect ◽  
Particle Swarm ◽  
Ensemble Classifier ◽  
Fast Convergence ◽  
Inspection System ◽  
Swarm Optimization ◽  
Surface Defect Detection

Study on the Parts Surface Defect Detection Method Based on Modifed SVM Algorithm

2014 ◽  
Vol 541-542 ◽  
pp. 1447-1451 ◽  
Author(s):  
Feng Ying Zhang ◽  
Rui Juan Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Defect Detection ◽  
Surface Defect ◽  
Particle Swarm ◽  
Classification Problem ◽  
Swarm Optimization ◽  
Parameters Selection ◽  
Svm Algorithm ◽  
Surface Defect Detection ◽  
Selection For

In this paper, we propose an efficient parts surface defect detection method using SVM algorithm, and particle swarm optimization is used to make parameters selection for SVM. The proposed parts surface defect detection systems is made up of six parts, and the main ideas of our method lie in that we exploit computer vision and machine learning in the research field of mechanical manufacturing and automation. We convert the parts surface defect detection problem to the classification problem, and the images of parts surface are used as testing samples. The SVM algorithm regards the classification problem as the constrained optimization problem. The classification accuracy is determined by the quality of parameters selection. Hence, particle swarm optimization is exploited to make parameters selection for SVM by defining two fitness functions. Afterwards, the best particle of the current population and the gbest is obtained. Utilizing the output from the particle swarm optimization then the parameters for SVM can be obtained. Finally, experiments are conducted based on a dataset with 563 samples, and experimental results illustrate that the proposed is quite effective for parts surface defect detection.

scholarly journals Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5136
Author(s):  
Xiaoxin Fang ◽  
Qiwu Luo ◽  
Bingxing Zhou ◽  
Congcong Li ◽  
Lu Tian
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Three Dimensional ◽  
Image Features ◽  
Stereoscopic Vision ◽  
Research Progress ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Defect Detection ◽  
Planar Materials

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

scholarly journals Fast Convergence Particle Swarm Optimization for Functions Optimization

2012 ◽  
Vol 4 ◽  
pp. 319-324 ◽  
Author(s):  
Amaresh Sahu ◽  
Sushanta Kumar Panigrahi ◽  
Sabyasachi Pattnaik
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fast Convergence ◽  
Swarm Optimization

A Deep Ensemble Classifier for Surface Defect Detection in Aircraft Visual Inspection

2020 ◽  
Vol 4 (1) ◽  
pp. 20200031
Author(s):  
Ivan Ren ◽  
Feraidoon Zahiri ◽  
Gregory Sutton ◽  
Thomas Kurfess ◽  
Christopher Saldana
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Ensemble Classifier ◽  
Surface Defect Detection

scholarly journals Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189382-189394
Author(s):  
Atiq Ur Rehman ◽  
Ashhadul Islam ◽  
Samir Brahim Belhaouari
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fast Convergence ◽  
Swarm Optimization

Surface defect detection for high-speed rails using an inverse P-M diffusion model

Sensor Review ◽  
2016 ◽  
Vol 36 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Zhendong He ◽  
Yaonan Wang ◽  
Feng Yin ◽  
Jie Liu
Keyword(s):  
Diffusion Model ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defect ◽  
Nearest Neighbor ◽  
Inspection System ◽  
Content Type ◽  
Difference Image ◽  
Vision Inspection ◽  
Surface Defect Detection

Purpose – When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed. Design/methodology/approach – First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects. Findings – Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments. Originality/value – A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

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