An intelligent defect detection system for warp-knitted fabric

2021 ◽  
pp. 004051752110600
Author(s):  
Xie Guosheng ◽  
Xu Yang ◽  
Yu Zhiqi ◽  
Sun Yize
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Detection System ◽  
Color Difference ◽  
Detection Algorithm ◽  
Inspection System ◽  
Knitted Fabric ◽  
Single Function ◽  
Inspection Systems ◽  
Warp Knitted Fabric

In textile factories, the most typical warp-knitted fabric defects include point defects, holes, and color differences. Traditional manual inspection methods are inefficient for detecting these defects. Existing intelligent inspection systems often have a single function. Factories require a real-time inspection system that can detect common defects and color difference. The YOLO (you only look once) neural network is faster than the two-stage neural network and has lower hardware requirements. The system’s color difference detection algorithm compares the color difference between the standard image and the image to be measured and records where the color difference value is exceeded. Finally, the comparison of the factory application proves that the designed system has good real-time performance and accuracy and can meet the fabric inspection requirements of warp-knitted fabric factories.

Research on Real-Time Vision Detection Method for Sanitary Labels

2013 ◽  
Vol 300-301 ◽  
pp. 484-489
Author(s):  
Chao Luo ◽  
Le Song ◽  
Mei Rong Zhao ◽  
Yu Chi Lin ◽  
Jian Li
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Inspection System ◽  
Matching Method ◽  
Accurate Identification ◽  
Vision Detection

Taking diaper which is a representative production of sanitary supplies as an example, a real-time detection method for diaper label based on machine vision is developed. To identify the location of diaper surface label position rapidly, a visual inspection system platform applies to production line is built. Images are captured with high-resolution colorful CCD industrial camera and NC template matching method is adopted as the surface label detection algorithm. Meanwhile, the comparative experiments results among NC, ABS method and Moment Matching method are presented. Experimental results show that this label detection system can realize accurate identification on the condition of different light, whose recognition rate can reach up to 97% and detection algorithm is of preferable instantaneity and stability.

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

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
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.

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

The Development and Design of a Laser Detection System for Tire Thickness Measurement

2013 ◽  
Vol 418 ◽  
pp. 128-131
Author(s):  
King Sun Lee
Keyword(s):  
Standard Deviation ◽  
Real Time ◽  
Detection System ◽  
Measurement Data ◽  
Thickness Measurement ◽  
Inspection System ◽  
Laser Sensors ◽  
Repeatability Error ◽  
On Line ◽  
Measuring Machine

This system is a self-developed real-time thickness inspection system including high-precision laser sensors and a mobile platform for on-line detection of tire rubber skin. The measurement data is used to calculate the standard deviation and process capability indices, and to evaluate measurement capacity. The system is a real-time measurement system in which the obtained measuring data compare with the standard value and show any errors. A technician can adjust the process parameters precisely on-line to improve product quality. The standard deviation of repeatability of the system for height is within +/- 0.0081 mm. The repeatability error of the horizontal sliding rail is within 0.0145mm, while the measurement error between this system and a coordinated measuring machine is within 0.028mm.

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

scholarly journals Real-Time Fuel Truck Detection Algorithm Based on Deep Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 118808-118817 ◽  
Author(s):  
Hamid R. Alsanad ◽  
Osman N. Ucan ◽  
Muhammad Ilyas ◽  
Atta Ur Rehman Khan ◽  
Oguz Bayat
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Network
Sign in / Sign up

Export Citation Format

Share Document