scholarly journals A Generic Automated Surface Defect Detection Based on a Bilinear Model

2019 ◽  
Vol 9 (15) ◽  
pp. 3159 ◽  
Author(s):  
Fei Zhou ◽  
Guihua Liu ◽  
Feng Xu ◽  
Hao Deng
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Automatic Classification ◽  
Input Image ◽  
Small Sample ◽  
Bilinear Model ◽  
Heat Map ◽  
Surface Defect Detection ◽  
Weakly Supervised

Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

Research on Surface Defect Detection Technique of Rolling Element Based on Computer Vision

2014 ◽  
Vol 1006-1007 ◽  
pp. 773-778 ◽  
Author(s):  
Chuan Ren ◽  
Xiao Yu Xiu ◽  
Guo Hui Zhou
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defect ◽  
Surface Defects ◽  
Median Filter ◽  
Image Sensor ◽  
Threshold Method ◽  
Rolling Element ◽  
Surface Defect Detection

This paper proposed a new method of surface defect detection of rolling element based on computer vision, which adopted CCD digital camera as image sensor, and used digital image processing techniques to defect the surface defects of rolling element. The main steps include collect image, use an improved median filter to reduce the noise, increase or decrease the exposure to achieve the image enhancement, create a binary image with threshold method and detect the edge of the image, and use subtraction method for surface defects identification. The experiment indicates that the above methods the advantages of simple, the capability of noise resistance, high speed processing and better real-time.

A Review Paper on Product Surface Defect Detection of Ironing Process

2016 ◽  
Vol 836 ◽  
pp. 147-152
Author(s):  
Akhmad Faizin ◽  
Arif Wahjudi ◽  
I. Made Londen Batan ◽  
Agus Sigit Pramono
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Forming Process ◽  
Metal Forming Process ◽  
Surface Defect Detection ◽  
Product Surface ◽  
Processing Techniques

The quality of product of manufacturing industries depends on dimension accurately and surface roughness quality. There are many types of surface defects and levels of surface roughness quality. Ironing process is one type of metal forming process, which aims to reduce the wall thickness of the cup-shaped or pipes products, thus increasing the height of the wall. Manually surface inspection procedures are very inadequate to ensure the surface in guaranteed quality. To ensure strict requirements of customers, the surface defect inspection based on image processing techniques has been found to be very effective and popular over the last two decades. The paper has been reviewed some papers based on image processing for defect detection. It has been tried to find some alternatives of useful methods for product surface defect detection of ironing process.

scholarly journals YOLOv4-MN3 for PCB Surface Defect Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11701
Author(s):  
Xinting Liao ◽  
Shengping Lv ◽  
Denghui Li ◽  
Yong Luo ◽  
Zichun Zhu ◽  
...  
Keyword(s):  
Defect Detection ◽  
High Performance ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Surface Defects ◽  
Activation Function ◽  
Production Quality ◽  
Circuit Board ◽  
Detection Accuracy ◽  
Surface Defect Detection

Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.

scholarly journals Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm

2019 ◽  
Vol 7 (4.14) ◽  
pp. 401
Author(s):  
Ze-Hao Wong ◽  
C. M. Thong ◽  
W. M. Edmund Loh ◽  
C. J. Wong
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Linear Filter ◽  
Wide Range ◽  
Surface Defect Detection ◽  
Design Variation ◽  
Industrial Lighting

Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.  

scholarly journals PSIC-Net: Pixel-Wise Segmentation and Image-Wise Classification Network for Surface Defects

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 221
Author(s):  
Linjian Lei ◽  
Shengli Sun ◽  
Yue Zhang ◽  
Huikai Liu ◽  
Wenjun Xu
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Performance Metrics ◽  
Surface Defects ◽  
Training Data ◽  
Detection Methods ◽  
Detection Technology ◽  
Surface Defect Detection ◽  
Public Datasets

Recent years have witnessed the widespread research of the surface defect detection technology based on machine vision, which has spawned various effective detection methods. In particular, the rise of deep learning has allowed the surface defect detection technology to develop further. However, these methods based on deep learning still have some drawbacks. For example, the size of the sample data is not large enough to support deep learning; the location and recognition of surface defects are not accurate enough; the real-time performance of segmentation and classification is not satisfactory. In the context, this paper proposes an end-to-end convolutional neural network model: the pixel-wise segmentation and image-wise classification network (PSIC-Net). With the innovative design of a three-stage network structure, improved loss function and a two-step training mode, PSIC-Net can accurately and quickly segment and classify surface defects with a small dataset of training data. This model was evaluated with three public datasets, and compared with the most advanced defect detection methods. All the performance metrics prove the effectiveness and advancement of PSIC-Net.

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