scholarly journals A NOVEL METHOD FOR INSPECTION DEFECTS IN COMMERCIAL EGGS USING COMPUTER VISION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 809-816
Author(s):  
Y. M. Valencia ◽  
J. J. Majin ◽  
V. B. Taveira ◽  
J. D. Salazar ◽  
M. E. Stivanello ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Inspection ◽  
Working Hours ◽  
Inspection System ◽  
Learning Approaches ◽  
Visual Fatigue ◽  
Long Working Hours ◽  
Novel Method ◽  
Classical Image

Abstract. The objective of this work is to compare the use of classical image processing approaches with deep learning approaches in a visual inspection system for defects in commercial eggs. Currently, many industries perform the detection of defects in eggs manually, this implies a large number of workers with long working hours who are exposed to visual fatigue and physical and mental discomfort. As a solution, this work proposes to develop an automatic inspection technique for defects in eggs using computer vision, capable of being operable in the industry. Different image processing approaches were evaluated in order to determine the best solution in terms of performance and processing time.

scholarly journals Fashion Product Classification through Deep Learning and Computer Vision

2019 ◽  
Vol 9 (7) ◽  
pp. 1385 ◽  
Author(s):  
Luca Donati ◽  
Eleonora Iotti ◽  
Giulio Mordonini ◽  
Andrea Prati
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Deep Learning ◽  
Template Matching ◽  
Learning Approaches ◽  
Visual Classification ◽  
Product Classification ◽  
Processing Techniques

Visual classification of commercial products is a branch of the wider fields of object detection and feature extraction in computer vision, and, in particular, it is an important step in the creative workflow in fashion industries. Automatically classifying garment features makes both designers and data experts aware of their overall production, which is fundamental in order to organize marketing campaigns, avoid duplicates, categorize apparel products for e-commerce purposes, and so on. There are many different techniques for visual classification, ranging from standard image processing to machine learning approaches: this work, made by using and testing the aforementioned approaches in collaboration with Adidas AG™, describes a real-world study aimed at automatically recognizing and classifying logos, stripes, colors, and other features of clothing, solely from final rendering images of their products. Specifically, both deep learning and image processing techniques, such as template matching, were used. The result is a novel system for image recognition and feature extraction that has a high classification accuracy and which is reliable and robust enough to be used by a company like Adidas. This paper shows the main problems and proposed solutions in the development of this system, and the experimental results on the Adidas AG™ dataset.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Soda Casket Inspection System by Computer Vision

2012 ◽  
Vol 622-623 ◽  
pp. 1425-1429
Author(s):  
Teerapong Orachon ◽  
Pattana Intani
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Risk ◽  
Edge Detection ◽  
Template Matching ◽  
Cross Correlation ◽  
Processing Method ◽  
Inspection System ◽  
Image Processing Method

Referred to the problem a manufacturer always faces that bottles in a casket are missing, caused by the bottles leaning down. Checking the parceling by human causes high risk from the explosion of the lean-down bottles. So this article presents how computer vision is applied to the inspection system. The system used a low-price webcam and image processing method to identify the defection. The process begins with adjusting HSV up before working on edge detection to reduce density of data. Then cross correlation as template matching to define the bottles and check the number of soda bottles. This system can identify 84% of completely loaded casket and 100% of the missing loaded casket.

scholarly journals Intelligent Image-Based Railway Inspection System Using Deep Learning-Based Object Detection and Weber Contrast-Based Image Comparison

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4738 ◽  
Author(s):  
Jinbeum Jang ◽  
Minwoo Shin ◽  
Sohee Lim ◽  
Jonggook Park ◽  
Joungyeon Kim ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Inspection System ◽  
Wide Field ◽  
Single Shot ◽  
Image Comparison ◽  
Line Scan ◽  
Region Detection ◽  
Sustainable Operation ◽  
Railway Infrastructure

For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. We installed line scan camera on the roof of the train. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention.

Reconfigurable Image Acquisition in the Machine Vision Inspection System

2012 ◽  
Vol 197 ◽  
pp. 376-380
Author(s):  
Da Xing Zhao ◽  
Lei Peng ◽  
Guo Dong Sun ◽  
Wei Feng
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Image Matching ◽  
Visual Inspection ◽  
Detection System ◽  
Image Acquisition ◽  
Inspection System ◽  
Image Detection ◽  
Vision Inspection ◽  
Machine Vision Inspection

Since camera drivers provided by the different manufacturers are not compatible, machine vision systems must be redeveloped according to specific camera. It is great significant to work out the problem, which could improve the versatility of the inspection system. The reconfigurable technology has applied to image processing, image matching and so on. Hence, in the paper the reconfigurable image acquisition module is designed, which reserves some interfaces for the image detection module. Citing the nonel visual inspection system as an example, adopting DALSA and BASLER cameras to acquire the images, the images was displayed properly. Therefore, the compatibility of the image detection system has been improved greatly.

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 Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Logistic Regression ◽  
Gabor Filter ◽  
Region Of Interest ◽  
Principal Component ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Logistic Regression Models ◽  
Moment Estimation

This study aims at proposing a computer vision model for automatic recognition of localized spall objects appearing on surfaces of reinforced concrete elements. The new model is an integration of image processing techniques and machine learning approaches. The Gabor filter supported by principal component analysis and k-means clustering is used for identifying the region of interest within an image sample. The binary gradient contour, gray level co-occurrence matrix, and color channels’ statistical measurements are employed to compute the texture of the extracted region of interest. Based on the computed texture-based features, the logistic regression model trained by the state-of-the-art adaptive moment estimation (Adam) is utilized to establish a decision boundary that delivers predictions on the status of “nonlocalized spall” and “localized spall.” Experimental results demonstrate that the newly developed model is able to achieve good detection accuracy with classification accuracy rate = 85.32%, precision = 0.86, recall = 0.79, negative predictive value = 0.85, and F1 score = 0.82. Thus, the proposed computer vision model can be helpful to assist decision makers in the task of the periodic survey of structure heath condition.

A Soldering Defect Inspection System of a Special Integrated Circuit Board Based on Computer Vision

2013 ◽  
Vol 650 ◽  
pp. 543-547
Author(s):  
Cong Ling Zhou ◽  
Jun Qiang Wu ◽  
Yong Qiang Wang ◽  
Zeng Pu Xu
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Integrated Circuit ◽  
Visual Inspection ◽  
Ccd Camera ◽  
Structure Design ◽  
Circuit Board ◽  
Inspection System ◽  
Defect Inspection ◽  
Hardware System

This paper introduces a soldering defect inspection system for a special integrated circuit board aided by the computer vision. Space occluder is fixed on this special integrated circuit board, which makes the light blocked from the CCD camera to the chip pins to be inspected. This system can inspect the light blocked soldering defects of the chip pins through the structure design of hardware system and the software system. It is a cheap but automatic soldering defect inspecting system, and can do the soldering defect detection instead of manual visual inspection, and improve the detection speed and stability.

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