Automated visual inspection of fabric image using deep learning approach for defect detection

2021 ◽  
Author(s):  
Vyacheslav V. Voronin ◽  
Roman Sizyakin ◽  
Marina Zhdanova ◽  
Evgenii A. Semenishchev ◽  
Dmitry Bezuglov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Inspection ◽  
Learning Approach ◽  
Automated Visual Inspection

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 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.

Layerwise Automated Visual Inspection in Laser Powder-Bed Additive Manufacturing

2015 ◽  
Author(s):  
Masoumeh Aminzadeh ◽  
Thomas Kurfess
Keyword(s):  
Additive Manufacturing ◽  
Defect Detection ◽  
Visual Inspection ◽  
Object Segmentation ◽  
Detection System ◽  
Automated Visual Inspection ◽  
Powder Bed ◽  
Geometric Objects ◽  
Powder Fusion

Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of precision as well as the formation of defects due to process randomness and irregularities associated with laser powder fusion. Over the past two decades much research has been conducted to control laser powder fusion in order to provide parts of higher quality. This paper addresses online quality monitoring in AM by in-situ automated visual inspection of each layer which is aimed to geometric objects and defects from high-resolution visual images. A scheme for online defect detection system is presented that consists of three levels of processing: low-level, intermediate-level, and high-level processing. Each level is described and appropriately divided to several stages, when insightful. Techniques that are feasible in each level for successful defect detection and classification are identified and described. Requirements and specifications of the measurement data to achieve desired performance of the online defect detection system are stated. Image processing algorithms are developed for first level of processing and implemented for segmentation of geometric objects. Due to the large variation of intensities within the powder region and fused regions, and also the non-multi-modal nature of the image, the basic segmentation algorithms such as thresholding do not produce appropriate results. In this work, morphological operations are effectively designed and implemented following thresholding to achieve the desired object segmentation. Examples of implementations are given. The paper provides the results of object segmentation which is the initial stage of development of an in-situ automated visual inspection for L-PBF process.

scholarly journals Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

2019 ◽  
Author(s):  
Ruofeng Wei ◽  
Yunbo Bi
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defects ◽  
Average Precision ◽  
Saliency Maps ◽  
Aluminum Profile ◽  
Safety And Reliability ◽  
Aluminum Profiles

Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

Simultaneous pixel-level concrete defect detection and grouping using a fully convolutional model

2021 ◽  
pp. 147592172098543
Author(s):  
Chaobo Zhang ◽  
Chih-chen Chang ◽  
Maziar Jamshidi
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defects ◽  
Semantic Segmentation ◽  
Input Image ◽  
Image Size ◽  
Bounding Box ◽  
Proposed Model ◽  
Image Pixels

Deep learning techniques have attracted significant attention in the field of visual inspection of civil infrastructure systems recently. Currently, most deep learning-based visual inspection techniques utilize a convolutional neural network to recognize surface defects either by detecting a bounding box of each defect or classifying all pixels on an image without distinguishing between different defect instances. These outputs cannot be directly used for acquiring the geometric properties of each individual defect in an image, thus hindering the development of fully automated structural assessment techniques. In this study, a novel fully convolutional model is proposed for simultaneously detecting and grouping the image pixels for each individual defect on an image. The proposed model integrates an optimized mask subnet with a box-level detection network, where the former outputs a set of position-sensitive score maps for pixel-level defect detection and the latter predicts a bounding box for each defect to group the detected pixels. An image dataset containing three common types of concrete defects, crack, spalling and exposed rebar, is used for training and testing of the model. Results demonstrate that the proposed model is robust to various defect sizes and shapes and can achieve a mask-level mean average precision ( mAP) of 82.4% and a mean intersection over union ( mIoU) of 75.5%, with a processing speed of about 10 FPS at input image size of 576 × 576 when tested on an NVIDIA GeForce GTX 1060 GPU. Its performance is compared with the state-of-the-art instance segmentation network Mask R-CNN and the semantic segmentation network U-Net. The comparative studies show that the proposed model has a distinct defect boundary delineation capability and outperforms the Mask R-CNN and the U-Net in both accuracy and speed.

scholarly journals Segmentation-based deep-learning approach for surface-defect detection

2019 ◽  
Vol 31 (3) ◽  
pp. 759-776 ◽  
Author(s):  
Domen Tabernik ◽  
Samo Šela ◽  
Jure Skvarč ◽  
Danijel Skočaj
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Learning Approach ◽  
Surface Defect Detection

Automated visual inspection of target parts for train safety based on deep learning

2018 ◽  
Vol 12 (6) ◽  
pp. 550-555 ◽  
Author(s):  
Fuqiang Zhou ◽  
Ya Song ◽  
Liu Liu ◽  
Dongtian Zheng
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Automated Visual Inspection ◽  
Train Safety

scholarly journals State of the Art in Defect Detection Based on Machine Vision

2021 ◽  
Author(s):  
Zhonghe Ren ◽  
Fengzhou Fang ◽  
Ning Yan ◽  
You Wu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Visual Inspection ◽  
State Of The Art ◽  
Image Acquisition ◽  
Detection Algorithms ◽  
Further Development

AbstractMachine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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