Multi-Stream Deep Convolutional Neural Network for PET Preform Surface Defects Detection and Classification

Zhang, Taochuan; Duan, Chongdi

doi:10.1109/access.2021.3128357

Cited by 2 publications

(5 citation statements)

References 35 publications

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“…We have conducted a comprehensive analysis comparing various DL-based methods for detecting surface defects [7], [8], [10], [26], [28], [40]. In 13.…”

Section: Comparison With Other Surface Defects Detection Methodsmentioning

confidence: 99%

“…Zhang et al [7] presented a multi-stream deep CNN to adequately recognize and classify various PET preform surface defects including black spots, bubbles, and scratches. Authors have adopted multi-stream feature fusion techniques in defect detection applications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These methods are lightweight and fast but provide relatively lesser accuracy in practical scenarios [5]. Another approach is the Deep Learning (DL) based automatic feature selection [6] method which is highly accurate in practical scenarios but at the expense of high computation complexity [7], [8]. The last CV module focuses on identifying defects or anomalies within the images by analyzing the extracted features to determine the presence and location of defects.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, this graph highlights that VGG-16, despite being the largest model, exhibits the smallest ASR value, while shufflenetV2, the smallest model, demonstrates the highest ASR value among all models. Approaches used to detect multiple defect classes[7],[26] are denoted by 'blue circles' and binary defect class[8] [10][28] [40]are represented by 'black circles' in Figure…”

mentioning

confidence: 99%

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A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

Kazmi,

Hafeez,

Aftab

et al. 2023

IEEE Access

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This paper presents a deep learning-based framework for automating the visual inspection of plastic bottles in an Industry 4.0 context, detecting surface defects to enhance product quality. Our contributions include the acceleration of model development through knowledge transfer learning, an inventive data generation strategy that combines physical samples with synthetic data augmentation techniques, an extensive evaluation of pre-trained deep convolutional neural networks, and a user-friendly interface for real-time quality inspection reporting and making the information easily accessible and actionable. In comparison to existing methods, our proposed method outperforms with a higher Accuracy to Size Ratio of 7.0. This characteristic underscores its capacity to efficiently and accurately classify and detect defects across multiple classes while maintaining a low area utilization. This feature not only demonstrates its exceptional performance but also positions it as a practical solution for real-world scenarios with resource constraints.

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“…We have conducted a comprehensive analysis comparing various DL-based methods for detecting surface defects [7], [8], [10], [26], [28], [40]. In 13.…”

Section: Comparison With Other Surface Defects Detection Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

Kazmi,

Hafeez,

Aftab

et al. 2023

IEEE Access

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show abstract

“…At the voxel level, tissue density (e.g., GM/WM) is commonly used as a feature for classification algorithms. 3D CNNs are typically employed in voxel-based methods [69]. Murcia et al [70] modeled the dataset by applying Convolutional Autoencoders (CAEs).…”

Section: G Neuroimaging Analysis Methodsmentioning

confidence: 99%

Longitudinal Characterization of Cerebral Hemodynamics in the TgF344-AD Rat Model of Alzheimer’s Disease

Fang

Tang

Zhang

et al. 2022

Preprint

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Alzheimer's Disease (AD) is a global healthcare crisis. The TgF344-AD rat is an AD model exhibiting age-dependent AD pathological hallmarks. We confirmed that AD rats developed cognitive deficits at 6 months without alteration of any other major biophysical parameters. We longitudinally characterized cerebral hemodynamics in AD rats at 3, 4, 6, and 14 months. The myogenic responses of the cerebral arteries and arterioles were impaired at 4 months of age in the AD rats. Consistent with the ex vivo results, the AD rat exhibited poor autoregulation of surface and deep cortical cerebral blood flow two months preceding cognitive decline. The dysfunction of cerebral hemodynamics in AD is exacerbated with age associated with reduced cerebral perfusion. Further, abolished cell contractility contributes to cerebral hemodynamics imbalance in AD. This may be attributed to enhanced ROS production, reduced mitochondrial respiration and ATP production, and disrupted actin cytoskeleton in cerebral vascular contractile cells.

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Multi-Stream Deep Convolutional Neural Network for PET Preform Surface Defects Detection and Classification

Cited by 2 publications

References 35 publications

A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

A Deep Learning-Based Framework for Visual Inspection of Plastic Bottles

Longitudinal Characterization of Cerebral Hemodynamics in the TgF344-AD Rat Model of Alzheimer’s Disease

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