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.

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.

Online metallic surface defect detection using deep learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1266-1273
Author(s):  
Feyza Cerezci ◽  
Serap Kazan ◽  
Muhammed Ali Oz ◽  
Cemil Oz ◽  
Tugrul Tasci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Metallic Surface ◽  
Surface Defect Detection

scholarly journals Automatic and efficient metallic surface defect detection based on key pixel point locations

2020 ◽  
pp. 1-1
Author(s):  
Jiahui Yu ◽  
Hongwei Gao ◽  
Jian Sun ◽  
Wei Yang ◽  
Yueqiu Jiang ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Metallic Surface ◽  
Surface Defect Detection

scholarly journals Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1562 ◽  
Author(s):  
Xiaoming Lv ◽  
Fajie Duan ◽  
Jia-jia Jiang ◽  
Xiao Fu ◽  
Lin Gan
Keyword(s):  
Defect Detection ◽  
Large Scale ◽  
Surface Defect ◽  
Data Augmentation ◽  
Training Data ◽  
Metallic Surface ◽  
Single Shot ◽  
Limited Data ◽  
Detection Model ◽  
Surface Defect Detection

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection.

scholarly journals Online Metallic Surface Defect Detection Using Deep LearningOnline Metallic Surface Defect Detection Using Deep Learning

2020 ◽  
Author(s):  
Feyza ÇEREZCİ ◽  
Serap KAZAN ◽  
Muhammed Ali Oz ◽  
Cemil Oz ◽  
Tugrul Tasci ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
System Stability ◽  
Metallic Surface ◽  
Surface Defect Detection ◽  
Image Acquisition System ◽  
Metallic Parts ◽  
Automated Quality Control

Abstract Across a range of manufacturing contexts, automated quality control has been gaining significantattention because it offers competitive advantages such as cost reduction, high accuracy in defect detection, and system stability over time. Although computer vision has historically been the most commonly applied method in this context, novel approaches such as deep learning have recently become more frequent and are used in cases where traditional methods cannot be applied. Because of the surface texture and curvature of many metallic parts, detection of defects such as scratches, cracks, and dents can be challenging for traditional computer vision methods. In this study, an image acquisition system supported by a special lighting device that provides processable images from an extremely reflective cylindrical metallic surface has been developed. Multiple images obtained from a single lateral line of the surface, which is rotated at a specified speed, are combined using photometric stereo and given as input to a convolutional neural network that is employed to classify defective and non-defective samples. The results obtained from this method are close to 98.5% accurate.

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.

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