scholarly journals Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4398 ◽  
Author(s):  
Jiahao Shi ◽  
Zhenye Li ◽  
Tingting Zhu ◽  
Dongyi Wang ◽  
Chao Ni
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Industrial Applications ◽  
Vital Role ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Wood Veneer ◽  
Multiple Channel ◽  
Execution Speed ◽  
Using Data

Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.

scholarly journals Metal Surface Defect Detection Using Modified YOLO

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 257
Author(s):  
Yiming Xu ◽  
Kai Zhang ◽  
Li Wang
Keyword(s):  
Neural Network ◽  
Metal Surface ◽  
Defect Detection ◽  
Surface Defect ◽  
Detection Methods ◽  
Cluster Center ◽  
Detection Accuracy ◽  
Detection Technology ◽  
Speed Advantage ◽  
Surface Defect Detection

Aiming at the problems of inefficient detection caused by traditional manual inspection and unclear features in metal surface defect detection, an improved metal surface defect detection technology based on the You Only Look Once (YOLO) model is presented. The shallow features of the 11th layer in the Darknet-53 are combined with the deep features of the neural network to generate a new scale feature layer using the basis of the network structure of YOLOv3. Its goal is to extract more features of small defects. Furthermore, then, K-Means++ is used to reduce the sensitivity to the initial cluster center when analyzing the size information of the anchor box. The optimal anchor box is selected to make the positioning more accurate. The performance of the modified metal surface defect detection technology is compared with other detection methods on the Tianchi dataset. The results show that the average detection accuracy of the modified YOLO model is 75.1%, which ia higher than that of YOLOv3. Furthermore, it also has a great detection speed advantage, compared with faster region-based convolutional neural network (Faster R-CNN) and other detection algorithms. The improved YOLO model can make the highly accurate location information of the small defect target and has strong real-time performance.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

Feature weighting network for aircraft engine defect detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2050012
Author(s):  
Liqiong Chen ◽  
Lian Zou ◽  
Cien Fan ◽  
Yifeng Liu
Keyword(s):  
Defect Detection ◽  
State Of The Art ◽  
Aircraft Engine ◽  
Air Transportation ◽  
Feature Weighting ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Practical Applications ◽  
Feature Pyramid ◽  
New Feature

Automatic aircraft engine defect detection is a challenging but important task in industry which can ensure safe air transportation and flight. In this paper, we propose a fast and accurate feature weighting network (FWNet) to solve the problem of defect scale variation and improve detection accuracy. The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications.

The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2044-2047
Author(s):  
Miao Yan ◽  
Zhi Bao Liu
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Improved Method ◽  
Software Faults ◽  
Component Software ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Detection Software

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

scholarly journals Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

2021 ◽  
pp. 1-14
Author(s):  
S. Rajesh Kannan ◽  
J. Sivakumar ◽  
P. Ezhilarasi
Keyword(s):  
Vital Role ◽  
Detection Methods ◽  
Occurrence Rate ◽  
Detection Accuracy ◽  
X Ray ◽  
Contagious Diseases ◽  
Disease Occurrence ◽  
Chest X Ray ◽  
Feature Based ◽  
Serially Concatenated

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of >  92% with SVM-RBF classifier and combining deep and machine features achieves >  96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

Methodology for Automatic Control of Automotive Al-Si Cast Components

2007 ◽  
Vol 539-543 ◽  
pp. 339-344 ◽  
Author(s):  
Mariusz Krupinski ◽  
Leszek Adam Dobrzański ◽  
Jerry Sokolowski ◽  
Wojciech Kasprzak ◽  
Glenn E. Byczynski
Keyword(s):  
Neural Networks ◽  
Quality Control ◽  
Defect Detection ◽  
Al Alloys ◽  
Detection Methods ◽  
Casting Method ◽  
Vacuum Casting ◽  
X Ray ◽  
Computer Based ◽  
Using Data

Computer based classification methodology is presented in the paper for defects being developed in the Al alloys as the car engine elements are made from them produced with the vacuum casting method. Identification of defects was carried out using data acquired from digital images obtained using the X-ray defect detection methods. The developed methodology as well as the related X-ray image analysis and quality control neural networks based software were carried out to solve this problem.

Research of Glass Fiber Textile Monitor Image Recognition Based on Neural Network

2013 ◽  
Vol 694-697 ◽  
pp. 1978-1982
Author(s):  
Shu Qian Chen ◽  
Yang Lie Fu
Keyword(s):  
Neural Network ◽  
Glass Fiber ◽  
Image Recognition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Textile Machine ◽  
Weft Fiber ◽  
The Stability ◽  
Monitor Image ◽  
Neural Network Input

Researched on weft fiber cut problems of glass fiber, improved the efficiency of textile production. Glass fiber textile machine is a major producer machine of glass fiber cloth. Textile machines weft detection usually uses the contact type in production, requires that the weft maintains certain pressure to the sensor. Using this method will cause glass fiber weft bristling, and will produce glass fiber floating dust. Damage to the textile machine and has the harm to the human body health. Used video surveillance method to detection the weft, image recognition and speed directly affects the stability of the system. This paper presented a detection methods of glass fiber textiles weft fiber cut based on neural network-based, selected multiple features which were directly related to the image with the weft as neural network input vector, through repeated training samples to remove tiny ripple effects which were caused by weft textile jitter, overcome the traditional method detection accuracy was not high. Experimental results show that this method can effectively avoid the weft jitter, making accurate detection of the weft fiber cut, and achieved satisfactory results.

scholarly journals Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1033
Author(s):  
Qiaodi Wen ◽  
Ziqi Luo ◽  
Ruitao Chen ◽  
Yifan Yang ◽  
Guofa Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Defect Detection ◽  
Region Of Interest ◽  
Aerial Images ◽  
Detection Methods ◽  
Complex Background

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

Mobile-Unet: An efficient convolutional neural network for fabric defect detection

2020 ◽  
pp. 004051752092860 ◽  
Author(s):  
Junfeng Jing ◽  
Zhen Wang ◽  
Matthias Rätsch ◽  
Huanhuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Production Efficiency ◽  
Detection Methods ◽  
Median Frequency ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Model Training

Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.

The Research on the Detection Methods of EMU Break Valves Features

2014 ◽  
Vol 687-691 ◽  
pp. 1034-1037
Author(s):  
Chun Ling Guan
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Characteristic Parameters ◽  
Spatial Function ◽  
Network Algorithm ◽  
Detection Technology ◽  
Neural Network Algorithm ◽  
Multiple Units

This paper focuses on the detection technology for Electric Multiple Units (EMU) break valves features. Aiming at the issues of EMU break valves features detection, this paper propose a kind of EMU break valves feature detection technology based on neural network algorithm which does not overly dependent on break valve characteristic parameters. The spatial function neural network algorithm is used to predict the EMU break valves features. The experiments illustrate the proposed algorithm can increase the detection accuracy with satisfactory effects in EMU break valves features detection.

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