Computer vision detection of foreign objects in coal processing using attention CNN

Zhang, Kanghui; Wang, Weidong; Lv, Ziqi; Fan, Yuhan; Song, Yang

doi:10.1016/j.engappai.2021.104242

Cited by 72 publications

(19 citation statements)

References 20 publications

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“…Zhang et al [23] detect the foreign objects in coal with computer vision using CNN. They proposed a model with an accuracy of 97% that can be used in real-time.…”

Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the nonuniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem have been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models.

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“…Zhang et al [23] detect the foreign objects in coal with computer vision using CNN. They proposed a model with an accuracy of 97% that can be used in real-time.…”

Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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“…Yan et al [36] proposed a hybrid artificial intelligence model combining a BP neural network (BPNN), genetic algorithm (GA), and adaptive boosting algorithm (AdaBoost), which could better evaluate the strength alteration of coal during CO 2 geological sequestration. In addition, artificial intelligence algorithms such as support vector machines and neural networks are often used by experts and scholars in the coal field for the deep mining of coal data information and training models [37,38].…”

Section: Research On Smart Energy In "Internet+" Modementioning

confidence: 99%

The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China

Dong

Zhang

Zhu

et al. 2021

IJERPH

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With the advent of the Energy 4.0 era, the adoption of "Internet + artificial intelligence" systems will enable the transformation and upgrading of the traditional energy industry. This will alleviate the energy and environmental problems that China is currently facing. The integrated development of artificial intelligence and the energy industry has become inevitable in the development of future energy systems. This study applied a comprehensive evaluation index to the energy industry to calculate the comprehensive development index of the energy industry in 30 provinces of China from 2000 to 2017. Then, taking Guangdong and Jiangsu as examples, the synthetic control method was used to explore the direction and intensity of the integrated development of artificial intelligence and the energy industry on the comprehensive development level of the local energy industry. The results showed that when artificial intelligence (AI) and the energy industry achieved a stable coupled development without the need to move to the coordination stage, the coupling effect promoted the development of the regional energy industry, and the annual growth rate of the comprehensive development index was above 20%. This coupling effect passed the placebo test and ranking test and was significant at the 10% level, indicating the robustness and validity of the experimental results, which strongly confirmed the great potential of AI in re-empowering traditional industries from the data perspective. Based on the findings, corresponding policy recommendations were proposed on how to promote the development of inter-regional AI, how the government, enterprises, and universities could cooperate to promote the coordinated development of AI and energy, and how to guide the integration process of regional AI and energy industries according to local conditions, in order to maximize the technological dividend of AI and help the construction of smart energy in China.

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“…In crack recognition problems, one of the key issues is the image segmentation [37,38]. High quality image segmentation is important as it can significantly simplify further object detection and recognition [39,40]. Within the current problem, the objects of interest are cracks and voids, which need to be separated from the background as efficiently as possible.…”

Section: Cracks Recognitionmentioning

confidence: 99%

Classification of Cracks in Composite Structures Subjected to Low-Velocity Impact Using Distribution-Based Segmentation and Wavelet Analysis of X-ray Tomograms

Wronkowicz-Katunin

Katunin

Nagode

et al. 2021

Sensors

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The problem of characterizing the structural residual life is one of the most challenging issues of the damage tolerance concept currently applied in modern aviation. Considering the complexity of the internal architecture of composite structures widely applied for aircraft components nowadays, as well as the additional complexity related to the appearance of barely visible impact damage, prediction of the structural residual life is a demanding task. In this paper, the authors proposed a method based on detection of structural damage after low-velocity impact loading and its classification with respect to types of acting stress on constituents of composite structures using the developed processing algorithm based on segmentation of 3D X-ray computed tomograms using the rebmix package, real-oriented dual-tree wavelet transform and supporting image processing procedures. The presented algorithm allowed for accurate distinguishing of defined types of damage from X-ray computed tomograms with strong robustness to noise and measurement artifacts. The processing was performed on experimental data obtained from X-ray computed tomography of a composite structure with barely visible impact damage, which allowed better understanding of fracture mechanisms in such conditions. The gained knowledge will allow for a more accurate simulation of structural damage in composite structures, which will provide higher accuracy in predicting structural residual life.

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Computer vision detection of foreign objects in coal processing using attention CNN

Cited by 72 publications

References 20 publications

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China

Classification of Cracks in Composite Structures Subjected to Low-Velocity Impact Using Distribution-Based Segmentation and Wavelet Analysis of X-ray Tomograms

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