Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision

Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to improve the automatic degree of surface defects detection in stamping grinding flat parts, a real-time detection system based on machine vision is designed. Under plane illumination mode, the whole region of the parts is clear and the outline is obvious, but the tiny defects are difficult to find; Under multi-angle illumination mode, the tiny defects of the parts can be highlighted. In view of the above situation, a lighting method combining plane illumination mode with multi-angle illumination mode is designed, and five kinds of defects are automatically detected by different detection methods. Firstly, the parts are located and segmented according to the plane light source image, and the defects are detected according to the gray anomaly. Secondly, according to the surface of the parts reflective characteristics, the influence of the reflection on the image is minimized by adjusting the exposure time of the camera, and the position and direction of the edge line of the gray anomaly region of the multi-angle light source image are used to determine whether the anomaly region is a defect. The experimental results demonstrate that the system has a high detection success rate, which can meet the real-time detection rEquation uirements of a factory.

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Real time detection system for rail surface defects based on machine vision

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-017-0241-y ◽

2018 ◽

Vol 2018 (1) ◽

Cited By ~ 25

Author(s):

Yongzhi Min ◽

Benyu Xiao ◽

Jianwu Dang ◽

Biao Yue ◽

Tiandong Cheng

Keyword(s):

Machine Vision ◽

Real Time ◽

Surface Defects ◽

Detection System ◽

Real Time Detection

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Real-Time Detection of Colour and Surface Defects of Maize Kernels Using Machine Vision

Journal of Agricultural Engineering Research ◽

10.1006/jaer.1994.1085 ◽

1994 ◽

Vol 59 (4) ◽

pp. 263-271 ◽

Cited By ~ 34

Author(s):

K. Liao ◽

Marvin R. Paulsen ◽

J.F. Reid

Keyword(s):

Machine Vision ◽

Real Time ◽

Surface Defects ◽

Real Time Detection ◽

Maize Kernels

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A new machine vision real-time detection system for liquid impurities based on dynamic morphological characteristic analysis and machine learning

Measurement ◽

10.1016/j.measurement.2018.04.015 ◽

2018 ◽

Vol 124 ◽

pp. 130-137 ◽

Cited By ~ 7

Author(s):

Xinyu Li ◽

Tiezhu Qiao ◽

Yusong Pang ◽

Haitao Zhang ◽

Gaowei Yan

Keyword(s):

Machine Learning ◽

Machine Vision ◽

Real Time ◽

Morphological Characteristic ◽

Detection System ◽

Characteristic Analysis ◽

Real Time Detection ◽

New Machine

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An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates

Applied Sciences ◽

10.3390/app9204222 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4222 ◽

Cited By ~ 3

Author(s):

Yang Liu ◽

Ke Xu ◽

Jinwu Xu

Keyword(s):

Real Time ◽

Surface Defects ◽

Recognition Accuracy ◽

Detection System ◽

Detection Algorithm ◽

Steel Plates ◽

Detection Accuracy ◽

Scale Invariant ◽

Speeded Up Robust Features ◽

Real Time Detection

The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system.

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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Sensors ◽

10.3390/s21165315 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5315

Author(s):

Chia-Pei Tang ◽

Kai-Hong Chen ◽

Tu-Liang Lin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Early Recognition ◽

Detection System ◽

Colon Polyp ◽

Colon Polyps ◽

Polyp Detection ◽

Detection Model ◽

Real Time Detection ◽

Polyp Type

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

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Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

Textile Research Journal ◽

10.1177/00405175211034241 ◽

2021 ◽

pp. 004051752110342

Author(s):

Sifundvolesihle Dlamini ◽

Chih-Yuan Kao ◽

Shun-Lian Su ◽

Chung-Feng Jeffrey Kuo

Keyword(s):

Machine Vision ◽

Real Time ◽

Defect Detection ◽

Data Augmentation ◽

Detection System ◽

Vision System ◽

Good Precision ◽

Machine Vision System ◽

Time Machine ◽

Textile Fabric

We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

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Real-time Detection System of Electrical Disturbances for Remote Communication Stations and Smart Grid

IEEE Latin America Transactions ◽

10.1109/tla.2017.8071233 ◽

2017 ◽

Vol 15 (10) ◽

pp. 1894-1900

Author(s):

Wesley Natanael Gallo ◽

Tales Heimfarth ◽

Danton Diego Ferreira ◽

Thais Martins Mendes

Keyword(s):

Smart Grid ◽

Real Time ◽

Detection System ◽

Remote Communication ◽

Real Time Detection

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Machine Vision Application on Science and Industry

Optoelectronics in Machine Vision-Based Theories and Applications - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5751-7.ch008 ◽

2019 ◽

pp. 233-254

Author(s):

Bassem S. M. Zohdy ◽

Mahmood A. Mahmood ◽

Nagy Ramadan Darwish ◽

Hesham A. Hefny

Keyword(s):

Machine Vision ◽

Medical Research ◽

Real Time ◽

Medical Images ◽

Deep Understanding ◽

Great Opportunity ◽

Material Systems ◽

Machine Vision Application ◽

Real Time Detection ◽

Vision Application

Machine vision studies opens a great opportunity for different domains as manufacturing, agriculture, aquaculture, medical research, also research studies and applications for better understanding of processes and operations. As scientists' efforts had been directed towards deep understanding of the particular material systems or particular classes of types of specific fruits, or diagnosis of patients through medical images classification and analysis, also real time detection and inspection of malfunction piece, or process, as various domains witnessed advancement through using machine vision techniques and methods.

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