scholarly journals Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

2021 ◽  
Vol 11 (4) ◽  
pp. 3093
Author(s):  
Victor Fernandez ◽  
Javier Chavez ◽  
Guillermo Kemper
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Visual Inspection ◽  
Fiber Optic ◽  
Electronic Device ◽  
Digital Camera ◽  
Optical Microscope ◽  
Processing Algorithm ◽  
Image Processing Algorithm

This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.

Engineering Realization of Dual-Target DPVS on Expressway Fog Detection

2014 ◽  
Vol 587-589 ◽  
pp. 2089-2095
Author(s):  
Qin Guan
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Digital Camera ◽  
Anhui Province ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Main Components ◽  
Dual Target ◽  
Fog Detection

Digital photographic visibility system (DPVS) based on binocular targets is a method using digital camera and image processing technology for visibility detection. In order to obtain accurate results, the function and installation methods for main components of video visibility detection system including artificial objects, cameras and other parts are designed delicately. Also the article explains the image processing algorithm. From this analysis, the detection system engineering and implementation are not complicated. In 2013, this video visibility detection method has been applied to enhance foggy area security system in Anhui Province, Bengbu - Huainan expressway, and achieved good results.

scholarly journals Design of Automated Visual Inspection System for Beverage Industry Production Line

2021 ◽  
Vol 38 (2) ◽  
pp. 461-466
Author(s):  
Subhransu Padhee ◽  
Durgesh Nandan
Keyword(s):  
Image Processing ◽  
Production Line ◽  
Visual Inspection ◽  
Conveyor Belt ◽  
Processing Algorithm ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Image Processing Algorithm ◽  
Overall Design ◽  
Beverage Industry

This paper provides an overall design and implementation perspective of a laboratory-scale automated visual inspection system for the beverage industry's production line. A case study has been undertaken where the image processing algorithm inspects the beverage bottle for any defects. Different defects such as improper labeling and improper liquid level can be detected using the image processing algorithm. A laboratory prototype of the conveyor belt has been built, and a prototype filling plant has been established to verify the simulation results.

scholarly journals Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

2013 ◽  
Vol 13 (4) ◽  
pp. 103-106 ◽  
Author(s):  
S.J. Świłło ◽  
M. Perzyk
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Surface Defects ◽  
Vision System ◽  
Shrinkage Porosity ◽  
Processing Algorithm ◽  
Shrinkage Cavity ◽  
Image Processing Algorithm ◽  
Defects Inspection ◽  
Surface Casting

Abstract The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

Sign in / Sign up

Export Citation Format

Share Document