scholarly journals Periodic Surface Defect Detection in Steel Plates Based on Deep Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3127 ◽  
Author(s):  
Yang Liu ◽  
Ke Xu ◽  
Jinwu Xu
Keyword(s):  
Defect Detection ◽  
Detection Rate ◽  
Short Term Memory ◽  
Steel Plates ◽  
Improved Method ◽  
Low Contrast ◽  
Defect Image ◽  
Surface Defect Detection ◽  
Lstm Network ◽  
Defect Recognition

It is difficult to detect roll marks on hot-rolled steel plates as they have a low contrast in the images. A periodical defect detection method based on a convolutional neural network (CNN) and long short-term memory (LSTM) is proposed to detect periodic defects, such as roll marks, according to the strong time-sequenced characteristics of such defects. Firstly, the features of the defect image are extracted through a CNN network, and then the extracted feature vectors are inputted into an LSTM network for defect recognition. The experiment shows that the detection rate of this method is 81.9%, which is 10.2% higher than a CNN method. In order to make more accurate use of the previous information, the method is improved with the attention mechanism. The improved method specifies the importance of inputted information at each previous moment, and gives the quantitative weight according to the importance. The experiment shows that the detection rate of the improved method is increased to 86.2%.

A Review of TFT-LCD Panel Defect Detection Methods

2013 ◽  
Vol 734-737 ◽  
pp. 2898-2902 ◽  
Author(s):  
Chuan Xia Jian ◽  
Jian Gao ◽  
Xin Chen
Keyword(s):  
Defect Detection ◽  
Detection Methods ◽  
Image Subtraction ◽  
Low Contrast ◽  
Defect Image ◽  
Key Issues ◽  
Segmentation Image ◽  
Fuzzy Boundary ◽  
Current Detection ◽  
Defect Boundary

TFT-LCD panel defect detection has been one of the difficulties in this field because of fuzzy defect boundary, low contrast between defects and background, and low detection speed. The structure of TFT-LCD panels and classification are introduced. Through the analysis of panel defect features, current detection methods for the TFT-LCD panel defects are reviewed. The key technologies of feature extraction and defect classification are analyzed in the defect image recognition of TFT-LCD panel. Meanwhile the methods of fuzzy boundary defect segmentation, image subtraction and image filtering are also discussed. Finally, the characteristics and advantages of these detection methods are concluded, and several key issues for the TFT-LCD defect detection have been proposed for future development.

scholarly journals A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4369 ◽  
Author(s):  
Tianyuan Liu ◽  
Jinsong Bao ◽  
Junliang Wang ◽  
Yiming Zhang
Keyword(s):  
Molten Pool ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Automatic Welding ◽  
Test Results ◽  
Welding Defects ◽  
Co2 Welding ◽  
Lstm Network ◽  
Defect Recognition ◽  
Pool Image

At present, realizing high-quality automatic welding through online monitoring is a research focus in engineering applications. In this paper, a CNN–LSTM algorithm is proposed, which combines the advantages of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The CNN–LSTM algorithm establishes a shallow CNN to extract the primary features of the molten pool image. Then the feature tensor extracted by the CNN is transformed into the feature matrix. Finally, the rows of the feature matrix are fed into the LSTM network for feature fusion. This process realizes the implicit mapping from molten pool images to welding defects. The test results on the self-made molten pool image dataset show that CNN contributes to the overall feasibility of the CNN–LSTM algorithm and LSTM network is the most superior in the feature hybrid stage. The algorithm converges at 300 epochs and the accuracy of defects detection in CO2 welding molten pool is 94%. The processing time of a single image is 0.067 ms, which fully meets the real-time monitoring requirement based on molten pool image. The experimental results on the MNIST and FashionMNIST datasets show that the algorithm is universal and can be used for similar image recognition and classification tasks.

A surface defect detection method via fusing multi-level features

2022 ◽  
pp. 1-17
Author(s):  
Meijian Ren ◽  
Rulin Shen ◽  
Yanling Gong
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Feature Fusion ◽  
Size Difference ◽  
Feature Maps ◽  
Low Contrast ◽  
Surface Defect Detection ◽  
Multi Level

Abstract Surface defect detection is very important to ensure product quality, but most of the surface defects of industrial products are characterized by low contrast, big size difference and category similarity, which brings challenges to the automatic detection of defects. To solve these problems, we propose a defect detection method based on convolutional neural network. In this method, a backbone network with semantic supervision is applied to extract the features of different levels. While a multi-level feature fusion module is proposed to fuse adjacent feature maps into high-resolution feature maps successively, which significantly improves the prediction accuracy of the network. Finally, an Encoding module is used to obtain the global context information of the high-resolution feature map, which further improves the pixel classification accuracy. Experiments show that the proposed method is superior to other methods in NEU_SEG (mIoU of 85.27) and MT (mIoU of 77.82) datasets, and has the potential of real-time detection.

scholarly journals Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

2018 ◽  
Vol 8 (9) ◽  
pp. 1575 ◽  
Author(s):  
Xian Tao ◽  
Dapeng Zhang ◽  
Wenzhi Ma ◽  
Xilong Liu ◽  
De Xu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
Low Noise ◽  
Machine Learning Techniques ◽  
Metallic Surface ◽  
Defect Inspection ◽  
Learning Techniques ◽  
Defect Image ◽  
Surface Defect Detection

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Surface defect detection of cylindrical lithium-ion battery by multiscale image augmentation and classification

2021 ◽  
pp. 2140011
Author(s):  
Harshad K. Dandage ◽  
Keh-Moh Lin ◽  
Horng-Horng Lin ◽  
Yeou-Jiunn Chen ◽  
Kun-San Tseng
Keyword(s):  
Lithium Ion Battery ◽  
Defect Detection ◽  
Surface Defect ◽  
Classification Scheme ◽  
Lithium Ion ◽  
Multi Scale ◽  
Image Patches ◽  
Stage Classification ◽  
Defect Image ◽  
Surface Defect Detection

While deep convolutional neural networks (CNNs) have recently made large advances in AI, the need of large datasets for deep CNN learning is still a barrier to many industrial applications where only limited data samples can be offered for system developments due to confidential issues. We thus propose an approach of multi-scale image augmentation and classification for training deep CNNs from a small dataset for surface defect detection on cylindrical lithium-ion batteries. In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage and to identify specific defect types in the second stage. The LSDD approach is an efficient prototyping method of defect detection from limited training images for quick system evaluation and deployment. The experiments show that, based on only 26 source images, the proposed LSDD (i) constructs two augmented multi-scale datasets of 19,309 and 6889 image patches for training and test, respectively, (ii) achieves 93.67% accuracy for discriminating defect image patches in the first stage, and (iii) reaches 90.78% mean precision rate and 93.89% mean recall rate for defect type identification in the second stage. Our two-stage classification scheme has higher defect detection sensitivity than an intuitive one-stage classification scheme by 0.69%, and outperforms the one-stage scheme in identifying specific defect types. For comparing with YOLOv3 detector, less defect misdetections are observed in our approach as well.

scholarly journals Defect Detection of Mechanical Design Products Using Deep Neural Network

2022 ◽  
Author(s):  
Mian Ahmad Jan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Industrial Production ◽  
Surface Defect ◽  
Mechanical Design ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Surface Defect Detection ◽  
Defect Recognition

Abstract In industrial production, defect detection is one of the key methods to control the quality of mechanical design products. Although defect detection algorithms based on traditional machine learning can greatly improve detection efficiency, manual feature extraction is required and the design process is complicated. With the rapid development of CNN, major breakthroughs have been made in computer vision. Therefore, building a surface defect detection algorithm for mechanical design products based on DCNNs plays a very important role in improving industrial production efficiency. This paper studies the surface defect detection algorithm of mechanical products based on deep convolutional neural network, focusing on solving two types of problems: defect recognition and defect segmentation. Aiming at the problem of defect recognition, this paper studies a defect recognition algorithm based on fully convolutional block detection. This algorithm introduces the idea of block detection into the ResNet fully convolutional neural network. While realizing the local discrimination mechanism, it overcomes the shortcomings of the traditional block detection receptive field. Compared with the original ResNet image classification algorithm, this algorithm has stronger generalization ability and detection ability of small defects. Aiming at the problem of defect segmentation, this paper studies a defect segmentation algorithm based on improved Deeplabv3+.

scholarly journals A Method of Surface Defect Detection of Irregular Industrial Products Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mengkun Li ◽  
Junying Jia ◽  
Xin Lu ◽  
Yue Zhang
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Characteristics ◽  
Detection Algorithm ◽  
Theory Analysis ◽  
Industrial Products ◽  
Detection Technology ◽  
Surface Defect Detection ◽  
Defect Recognition

In recent years, the surface defect detection technology of irregular industrial products based on machine vision has been widely used in various industrial scenarios. This paper takes Bluetooth headsets as an example, proposes a Bluetooth headset surface defect detection algorithm based on machine vision to quickly and accurately detect defects on the headset surface. After analyzing the surface characteristics and defect types of Bluetooth headsets, we proposed a surface scratch detection algorithm and a surface glue-overflowed detection algorithm. The result of the experiment shows that the detection algorithm can detect the surface defect of Bluetooth headsets fast as well as effectively, and the accuracy of defect recognition reaches 98%. The experiment verifies the correctness of the theory analysis and detection algorithm; therefore, the detection algorithm can be used in the recognition and detection of surface defect of Bluetooth headsets.

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