Multi-scale Defect Detection Network for Tire Visual Inspection

2021 ◽  
pp. 771-782
Author(s):  
Minghua Wei ◽  
Ren Wang ◽  
Qiang Guo
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Multi Scale

Image Defect Detection Methods for Visual Inspection Systems

2007 ◽  
Author(s):  
L. Tomczak ◽  
V. Mosorov ◽  
D. Sankowski ◽  
J. Nowakowski
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Detection Methods ◽  
Inspection Systems ◽  
Image Defect

scholarly journals A Study of Defect Detection Techniques for Metallographic Images

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5593 ◽  
Author(s):  
Wei-Hung Wu ◽  
Jen-Chun Lee ◽  
Yi-Ming Wang
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Network Architecture ◽  
Recognition Performance ◽  
Metallographic Analysis ◽  
Automatic Identification ◽  
Detection Techniques ◽  
Multi Scale ◽  
Modified Method ◽  
Metallographic Images

Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.

scholarly journals Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

2021 ◽  
Vol 11 (22) ◽  
pp. 10508
Author(s):  
Chaowei Tang ◽  
Xinxin Feng ◽  
Haotian Wen ◽  
Xu Zhou ◽  
Yanqing Shao ◽  
...  
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Automobile Industry ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
The Body ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Edge Features ◽  
Automobile Wheel

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

scholarly journals Real-time fabric defect detection based on multi-scale convolutional neural network

2020 ◽  
Vol 2 (4) ◽  
pp. 189-196
Author(s):  
Shuxuan Zhao ◽  
Li Yin ◽  
Jie Zhang ◽  
Junliang Wang ◽  
Ray Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Defect Detection ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Pulsed Thermography Inspection of Composite Anticorrosive Coatings: Defect Detection and Analysis of Their Thermal Behavior through Computational Simulation

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4812
Author(s):  
Marcella Grosso ◽  
Isabel C. P. Margarit-Mattos ◽  
Gabriela R. Pereira
Keyword(s):  
Thermal Behavior ◽  
Defect Detection ◽  
Visual Inspection ◽  
Composite Coatings ◽  
Computational Simulation ◽  
Internal Surface ◽  
Metallic Materials ◽  
Pulsed Thermography ◽  
Anticorrosive Coatings ◽  
Non Destructive

The use of anticorrosive coatings has been a powerful method to be applied on the surface of metallic materials to mitigate the corrosive process. In this study, the focus is composite coatings that are commonly used on the internal surface of storage tanks in petrochemical industries. The development of non-destructive methods for inspection of faults in this field is desired due to unhealthy access and mainly because undercoating corrosion is difficult to detect by visual inspection. Pulsed thermography (PT) was employed to detect undercoating corrosion and adhesion loss of anticorrosive composite coatings defects. Additionally, a computational simulation model was developed to complement the PT tests. According to the experimental results, PT was able to detect all types of defects evaluated. The results obtained by computational simulation were compared with experimental ones. Good correlation (similarity) was verified, regarding both the defect detection and thermal behavior, validating the developed model. Additionally, by reconstructing the thermal behavior according to the defect parameters evaluated in the study, it was estimated the limit of the remaining thickness of the defect for which it would be possible to obtain its detection using the pulsed modality.

Neighborhood-preserving cross correlation for automated visual inspection of fine-structured textile fabrics

2011 ◽  
Vol 81 (19) ◽  
pp. 2033-2042 ◽  
Author(s):  
A. S. Tolba
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Cross Correlation ◽  
Gabor Filter ◽  
Probabilistic Neural Network ◽  
Computational Cost ◽  
Detection Accuracy ◽  
Automated Visual Inspection ◽  
Textile Fabrics ◽  
Discriminant Power

The automated visual inspection of homogeneous flat surface products is a challenging task that needs fast and accurate algorithms for defect detection and classification in real time. Multi-directional and Multi-scale approaches, such as Gabor Filter Banks and Wavelets, have high computational cost in addition to their average performance in defect characterization. This paper presents a novel implementation of a neighborhood-preserving approach for the fast and accurate inspection of fine-structured industrial products using a new neighborhood-preserving cross-correlation feature vector. The fast and noise immune Probabilistic Neural Network (PNN) classifier has been found to be very suitable for defect detection in homogeneous non-patterned surfaces with acceptable slight variations, such as textile fabrics. A defect detection accuracy of 99.87% has been achieved with 99.29% recall/sensitivity and 99.91% specificity. The discriminant power shows how well the PNN classifier discriminates between normal and abnormal surfaces. The experimental results show that the proposed system outperforms the Gabor function-based techniques.

scholarly journals Automatic Fabric Defect Detection Method Using PRAN-Net

2020 ◽  
Vol 10 (23) ◽  
pp. 8434
Author(s):  
Peiran Peng ◽  
Ying Wang ◽  
Can Hao ◽  
Zhizhong Zhu ◽  
Tong Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Inspection Time ◽  
Feature Maps ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Quality Process

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

A Deep Ensemble Classifier for Surface Defect Detection in Aircraft Visual Inspection

2020 ◽  
Vol 4 (1) ◽  
pp. 20200031
Author(s):  
Ivan Ren ◽  
Feraidoon Zahiri ◽  
Gregory Sutton ◽  
Thomas Kurfess ◽  
Christopher Saldana
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Ensemble Classifier ◽  
Surface Defect Detection

scholarly journals Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7287
Author(s):  
Povendhan Palanisamy ◽  
Rajesh Elara Mohan ◽  
Archana Semwal ◽  
Lee Ming Jun Melivin ◽  
Braulio Félix Félix Gómez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Structural Defect ◽  
Visual Inspection ◽  
Field Trials ◽  
Detection Task ◽  
Robot Assisted ◽  
Inspection Robot ◽  
Reconfigurable Robot ◽  
Learning Frameworks

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.

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