An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

Xia, Fang; Wang, Jie; Feng, Tao

doi:10.3390/s19112636

Cited by 8 publications

(4 citation statements)

References 44 publications

(56 reference statements)

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“…Faithpraise et al [43] extracted structural information and used ANN to construct an automatic detection algorithm for copepod species. They assessed an overall accuracy of 93.13 % using seven copepod traits from 240 reference photos.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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In comparison to the competitors, engineers must provide quick, low-cost, and dependable solutions. The advancement of intelligence generated by machines and its application in almost every field has created a need to reduce the human role in image processing while also making time and labor profit. Lepidopterology is the discipline of entomology dedicated to the scientific analysis of caterpillars and the three butterfly superfamilies. Students studying lepidopterology must generally capture butterflies with nets and dissect them to discover the insect’s family types and shape. This research work aims to assist science students in correctly recognizing butterflies without harming the insects during their analysis. This paper discusses transfer-learning-based neural network models to identify butterfly species. The datasets are collected from the Kaggle website, which contains 10,035 images of 75 different species of butterflies. From the available dataset, 15 unusual species were selected, including various butterfly orientations, photography angles, butterfly lengths, occlusion, and backdrop complexity. When we analyzed the dataset, we found an imbalanced class distribution among the 15 identified classes, leading to overfitting. The proposed system performs data augmentation to prevent data scarcity and reduce overfitting. The augmented dataset is also used to improve the accuracy of the data models. This research work utilizes transfer learning based on various convolutional neural network architectures such as VGG16, VGG19, MobileNet, Xception, ResNet50, and InceptionV3 to classify the butterfly species into various categories. All the proposed models are evaluated using precision, recall, F-Measure, and accuracy. The investigation findings reveal that the InceptionV3 architecture provides an accuracy of 94.66%, superior to all other architectures.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Orban

Vadivel³

et al. 2022

Electronics

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“…e entire network can be divided into two stages: defect detection stage and defect classification stage. Fang et al [20] propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. Park et al [21] propose a convolutional neural network (CNN) based method that inspects nonpatterned welding defects (craters, pores, foreign substances, and fissures) on the surface of the engine transmission using a single RGB camera.…”

Section: Introductionmentioning

confidence: 99%

Bearing Defect Detection with Unsupervised Neural Networks

Zuo

Danchao³

et al. 2021

Shock and Vibration

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

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“…Because of the small size of retinal vessel, surgical robot and machine vision detection [12] are essential for cannulation. Smet et al injected ocriplasmin into porcine eyes to clear clot by robot-assisted cannulation [13], and Willekens et al performed cannulation to release drugs for more than 3 min and dissolved clot in 15 of 18 porcine eyes with the help of surgical robot [14].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Thrombolysis in Robot-Assisted Retinal Vein Cannulation

Pan

et al. 2020

Complexity

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Robot-assisted retinal cannulation is an eye surgical procedure which can dissolve the obstruction by using robot to inject anticoagulant into occluded vessel. The current research on the critical parameters of cannulation for human is scarce because of the immature technology. Considering the influence of microneedle, this work investigated the effects of drug concentration, injection velocity, injection position, and size of clot on cannulation by theoretical analysis and finite element analysis. For finite element analysis, the multiphysics continuum model was established to demonstrate species transport and reaction which simulates the entire lytic process of the occlusive clot, and four cell zones were established to describe the generation of plasmin (PLS) with the addition of tissue-type plasminogen activator (tPA) and fibrinolysis of clot by importing subroutines into each cell zone under the conditions of constant clot size and variable size, respectively. The results imply that the most efficient value of tPA concentration is 50 nM, injection velocity is 60 mm/s for clot length of 0.1 mm, and the best position to insert the cannula is 0.5 mm in front of the thrombus. For different clot lengths of 0.1 mm to 0.6 mm, the optimal range of tPA concentration and injection velocity is from 20 nM to 70 nM and from 40 mm/s to 60 mm/s, respectively, and explores the reasonable injection position of 0.3 mm to 0.5 mm in front of clot in a vein of 100 μm. This conclusion can be used to perform robot-assisted cannulation surgery to improve fibrinolytic efficiency.

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An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

Cited by 8 publications

References 44 publications

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

A Novel Method for the Classification of Butterfly Species Using Pre-Trained CNN Models

Bearing Defect Detection with Unsupervised Neural Networks

Numerical Simulation of Thrombolysis in Robot-Assisted Retinal Vein Cannulation

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