Simulation of the Mechanism of Insect Bionic Compound Eye Super-Resolution Restoration Technology of Copper Strip Surface Defect Images

2013 ◽  
Vol 462-463 ◽  
pp. 302-307
Author(s):  
Xiao Dan Sun ◽  
Xin Nan Fan ◽  
Ming Qiang Ling ◽  
Hong Hai Zhuang ◽  
Zhuo Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Compound Eye ◽  
Super Resolution ◽  
Reconstruction Technique ◽  
Copper Strip ◽  
Strip Surface ◽  
Imaging Technology ◽  
Inspection Process ◽  
Defect Image ◽  
Surface Defect Detection

In view of the problem that in traditional copper strip surface defect inspection process, the low resolution of the collected images will greatly decrease the accuracy of the detect defects, through the study of biological bionic imaging technology, combined with insect bionic compound eye imaging technology, this paper proposes a visual simulation insects bionic mechanism of the copper strip surface defect image super-resolution reconstruction technique. Through the study of biological bionic imaging technology, and take the advantage of insect compound eye visual imaging mechanism, this paper uses multiple linear array CCD image sensors to collect images aimed at getting defect images in all perspectives. The actual input images are restored by the method of super-resolution reconstruction using the sample library to improve the resolution of the image. Through the large amount of experiments of different copper strip surface defect images, and then compare the results, it can be seen that taking this papers method to dispose the defect images, will improve the images PSNR value, and has greatly improved the images quality, which will do good to improve the accuracy of the copper strip surface defect detection.

A lightweight detector based on attention mechanism for aluminum strip surface defect detection

2022 ◽  
Vol 136 ◽  
pp. 103585
Author(s):  
Zhuxi MA ◽  
Yibo Li ◽  
Minghui Huang ◽  
Qianbin Huang ◽  
Jie Cheng ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Attention Mechanism ◽  
Strip Surface ◽  
Aluminum Strip ◽  
Surface Defect Detection

A fast learning-based super-resolution method for copper strip defect image

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740042 ◽  
Author(s):  
Zhuo Zhang ◽  
Xinnan Fan ◽  
Xuewu Zhang
Keyword(s):  
Detection System ◽  
Super Resolution ◽  
Textural Features ◽  
Resolution Method ◽  
Copper Strip ◽  
Fast Learning ◽  
Efficiency And Effectiveness ◽  
Resolution Model ◽  
Defect Image ◽  
Weak Target

In this paper, a fast pre-classified-based super-resolution model has been proposed to overcome the problems of degraded imaging in weak-target real-time detection system, specialized to copper defect detection. To accurately characterize the defected image, textural features based on the statistical function of gray-gradient are presented. Furthermore, to improve the effectiveness and practicality of the online detection, a concept of pre-classified learning is introduced and an edge smoothness rule is designed. Some experiments are carried out on defect images in different environments and the experimental results show the efficiency and effectiveness of the algorithm.

scholarly journals Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 8945
Author(s):  
Yanghuan Xu ◽  
Dongcheng Wang ◽  
Bowei Duan ◽  
Huaxin Yu ◽  
Hongmin Liu
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Surface Defect ◽  
Surface Defects ◽  
Learning Strategy ◽  
Confusion Matrix ◽  
Automatic Detection ◽  
Future Research ◽  
Copper Strip ◽  
Strip Surface

Surface defect automatic detection has great significance for copper strip production. The traditional machine vision for surface defect automatic detection of copper strip needs artificial feature design, which has a long cycle, and poor ability of versatility and robustness. However, deep learning can effectively solve these problems. Therefore, based on the deep convolution neural network and the transfer learning strategy, an intelligent recognition model of surface defects of copper strip is established in this paper. Firstly, the defects were classified in accordance with the mechanism and morphology, and the surface defect dataset of copper strip was established by comprehensively adopting image acquisition and image augmentation. Then, a two-class discrimination model was established to achieve the accurate discrimination of perfect and defect images. On this basis, four CNN models were adopted for the recognition of defect images. Among these models, the EfficientNet model through transfer learning strategy had the best comprehensive performance with a recognition accuracy rate of 93.05%. Finally, the interpretability and deficiency of the model were analysed by the class activation map and confusion matrix, which point toward the direction of further optimization for future research.

scholarly journals Real-time system for automatic cold strip surface defect detection

2019 ◽  
Vol 47 (4) ◽  
pp. 765-774 ◽  
Author(s):  
Pavel Kostenetskiy ◽  
Rustem Alkapov ◽  
Nikita Vetoshkin ◽  
Roman Chulkevich ◽  
Ilya Napolskikh ◽  
...  
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Surface Defect ◽  
Time System ◽  
Real Time System ◽  
Strip Surface ◽  
Surface Defect 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 X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

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