Surface Inspection System of Steel Strip Based on Machine Vision

2009 ◽  
Author(s):  
Bo Tang ◽  
Jian-yi Kong ◽  
Xing-dong Wang ◽  
Li Chen
Keyword(s):  
Machine Vision ◽  
Steel Strip ◽  
Inspection System ◽  
Surface Inspection

scholarly journals Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

2018 ◽  
Vol 8 (12) ◽  
pp. 2565 ◽  
Author(s):  
Shengping Wen ◽  
Zhihong Chen ◽  
Chaoxian Li
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Inspection System ◽  
Mechanical Transmission ◽  
Surface Inspection ◽  
Machine Vision System ◽  
Factors Affecting ◽  
Bearing Rollers

Bearings are commonly used machine elements and an important part of mechanical transmission. They are widely used in automobiles, airplanes, and various instruments and equipment. Bearing rollers are the most important components in a bearing and determine the performance, life, and stability of the bearing. In order to control the surface quality of the rollers, a machine vision system for bearing roller surface inspection is proposed. We briefly introduced the design of the machine vision system and then focused on the surface inspection algorithm. We proposed a multi-task convolutional neural network to detect defects. We extracted the features of the defects through a shared convolutional neural network, then classified the defects and calculated the position of the defects simultaneously. Finally, we determined if the bearing roller was qualified according to the position, category, and area of the defect. In addition, we explored various factors affecting performance and conducted a large number of experiments. We compared our method with the traditional methods and proved that our method had good stability and robustness.

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

Innovative Surface Inspection System Deployment

2021 ◽  
Author(s):  
L. Alberto De Oliveira ◽  
B. Feagan ◽  
B. Isbell ◽  
B. Masters ◽  
A. Perrichon ◽  
...  
Keyword(s):  
Inspection System ◽  
Surface Inspection
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