Automated Visual Inspection of Surface Defects on Hot-Rolled Plate

2011 ◽  
Vol 201-203 ◽  
pp. 1619-1622
Author(s):  
Qiang Song
Keyword(s):  
Visual Inspection ◽  
Surface Defects ◽  
Digital Camera ◽  
Structural Defects ◽  
Rolled Plate ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Accurate Identification ◽  
Surface Defect Detection ◽  
Hot Rolled

This paper is concerned with the problem of automatic inspection of hot-rolled plate surface using machine vision. An automated visual inspection (AVI) system has been developed to take images of external hot-rolled plate surfaces and the detailed characteristics of the sensor system which include the illumination and digital camera are described. An intelligent surface defect detection paradigm based on morphology is proposed to detect structural defects on plate surfaces. The proposed method has been implemented and tested on a number of hot-rolled plate surfaces. The results suggest that the method can provide an accurate identification to the defects and can be developed into a commercial visual inspection system.

Progress toward an automated visual inspection system

1990 ◽  
Author(s):  
P. COLEMAN ◽  
S. NELSON ◽  
J. MARAM ◽  
A. NORMAN
Keyword(s):  
Visual Inspection ◽  
Inspection System ◽  
Automated Visual Inspection

Automated Visual Inspection of Contact Gap and Contact Malposition of Automotive Relay

2012 ◽  
Vol 190-191 ◽  
pp. 661-665 ◽  
Author(s):  
Chen Huei Hsieh ◽  
Chi Sheng Tsai ◽  
Ting Yu Tseng ◽  
Yi Sheng Wong ◽  
Shi Zhen Zhou
Keyword(s):  
Visual Inspection ◽  
Electrical Contact ◽  
Testing Equipment ◽  
Inspection System ◽  
Automation System ◽  
Automated Manufacturing ◽  
Automated Visual Inspection

The gap and the malposition of the contact of automotive relay would heavily influence its life. If the gap and the malposition of all relays can be fully inspected and the inspection operation can be incorporated into the existing automated manufacturing and testing equipment, the quality will thus be significantly promoted. To reach this goal, a visual inspection system based on the platform of LabVIEW has been developed in this paper. The visual inspection system is capable of performing the inspection of the gap and the malposition of electrical contact in 1.6 seconds for one relay, and the whole automation system can manufacture one relay for every 2 seconds.

scholarly journals Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

2019 ◽  
Vol 9 (22) ◽  
pp. 4898 ◽  
Author(s):  
Augustas Urbonas ◽  
Vidas Raudonis ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Visual Inspection ◽  
Surface Defects ◽  
Inspection System ◽  
Wood Veneer ◽  
Wood Panel ◽  
Wood Processing ◽  
Panel Surface

In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

A Real-Time Automated Visual Inspection System for Hot Steel Slabs

1983 ◽  
Vol PAMI-5 (6) ◽  
pp. 563-572 ◽  
Author(s):  
Bindinganavle R. Suresh ◽  
Richard A. Fundakowski ◽  
Tod S. Levitt ◽  
John E. Overland
Keyword(s):  
Real Time ◽  
Visual Inspection ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Steel Slabs

Automated visual inspection system for bonded IC wires

1993 ◽  
Vol 24 (6) ◽  
pp. 625-633 ◽  
Author(s):  
Hiroyuki Tsukahara ◽  
Masato Nakashima ◽  
Takehisa Sugawara
Keyword(s):  
Visual Inspection ◽  
Inspection System ◽  
Automated Visual Inspection

scholarly journals Implementasi Adaptive Neural Fuzzy Inference System (ANFIS) Untuk Menguji Kualitas Printed Circuit Board (PCB)

2021 ◽  
Vol 5 (2) ◽  
pp. 79-90
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Board ◽  
Fuzzy Inference ◽  
Circuit Board ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Inference System ◽  
Printed Circuit ◽  
Bahasa Indonesia ◽  
Neural Fuzzy

Pengujian kualitas menggunakan teknik pengolahan citra dan kecerdasan tiruan banyak diterapkan dalam berbagai industri, misalnya industri tekstil, perakitan kendaraan, makanan, minuman, perakitan elektronik, dan lain – lain. Pengujian model ini sering disebut dengan istilah Automated Visual Inspection System (AVIS) atau dalam bahasa Indonesia Sistem Inspeksi Visual Otomatis (SIVO). Penelitian ini mengacu pada model sistem inspeksi, di mana objek pengujiannya adalah keping Printed Circuit Board (PCB). Banyak penelitian tentang pengujian PCB yang sudah dilakukan, tetapi masih banyak yang belum memberikan hasil yang optimum, diantaranya waktu akses yang masih lambat, keakuratan data masih rendah, dan tingkat kesalahan yang masih tinggi. Berdasarkan hasil penelitian dan pengujian yang sudah dilakukan, model ANFIS sangat layak dijadikan sebagai model inferensi kecerdasan buatan dalam sistem yang berbasis inspeksi otomatis khususnya menguji kualitas keping PCB, karena terbukti model ANFIS dengan model hybrid trapesium mf memiliki tingkat kesalahan yang sangat kecil yaitu 4.0186e-007 dan untuk tingkat akurasi pengujian datanya mencapai 99%. 

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