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Detection and Classification of Defective Hard Candies Based on Image Processing and Convolutional Neural Networks

https://doi.org/10.3390/electronics10162017

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Segmentation of Partially Overlapping Nanoparticles Using Concave Points

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Comparison between soft computing methods for tomato quality grading using machine vision

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Detection and Classification of Potato Defects Using Multispectral Imaging System Based on Single Shot Method

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Minimum cross entropy thresholding

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A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing

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Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system

inferredcites →https://doi.org/10.1016/j.biosystemseng.2019.12.003openalex-snapshot

Computer vision based detection of external defects on tomatoes using deep learning

inferredcites →https://doi.org/10.1016/j.foodcont.2019.06.021openalex-snapshot

Assessment of macadamia kernel quality defects by means of near infrared spectroscopy (NIRS) and nuclear magnetic resonance (NMR)

inferredcites →https://doi.org/10.1016/j.jcs.2019.05.010openalex-snapshot

Colored rice quality inspection system using machine vision

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Machine vision system for classification of bulk raisins using texture features

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A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier

inferredcites →https://doi.org/10.1016/j.scienta.2019.109133openalex-snapshot

Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks

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An iterative algorithm for minimum cross entropy thresholding

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Random Forests

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Rapidly and exactly determining postharvest dry soybean seed quality based on machine vision technology

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Direct least square fitting of ellipses

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Gradient-based learning applied to document recognition

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot

MobileNetV2: Inverted Residuals and Linear Bottlenecks

inferredcites →https://doi.org/10.1109/cvpr.2019.00293openalex-snapshot

MnasNet: Platform-Aware Neural Architecture Search for Mobile

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Curvature scale space corner detector with adaptive threshold and dynamic region of support

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Robust Biometric Recognition From Palm Depth Images for Gloved Hands

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Semi-supervised text classification using enhanced KNN algorithm

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ImageNet classification with deep convolutional neural networks

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Research on tobacco foreign body detection device based on machine vision

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iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

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A Carrot Sorting System Using Machine Vision Technique

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Detection of Surface and Subsurface Defects of Apples Using Structured- Illumination Reflectance Imaging with Machine Learning Algorithms

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Apple Grading Method Based on Features of Color and Defect

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A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization

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An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets

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Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

inferredcites →https://doi.org/10.3390/met11030388openalex-snapshot

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.3390/s20236993openalex-snapshot

Defect Classification of Green Plums Based on Deep Learning

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Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

inferredcites →https://doi.org/10.3390/s21020392openalex-snapshot

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.12688/openreseurope.16792.1zenodo-depositor-declared

Hard Candy Production and Quality Parameters: A review

inferred← citeshttps://doi.org/10.2139/ssrn.4797854openalex-extracted

Monitoring Zno Surge Arrester Using Convolutional Neural Networks and Image Processing

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English Word Recognition System Based on BP Neural Network

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Extraction and Protection Strategy of Landscape Ecological and Cultural Characteristics Based on Deep Learning Algorithm

inferred← citeshttps://doi.org/10.1007/s11042-023-18060-4openalex-snapshot

An automatic segmentation algorithm for conglutinated bone fragments in 3D CT images of lower limb fractures based on morphology

inferred← citeshttps://doi.org/10.1016/j.compag.2022.107575openalex-snapshot

Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine

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Examination of bottle caps guided with image processing and deep neural networks

inferred← citeshttps://doi.org/10.1109/icprs58416.2023.10179072openalex-snapshot

Candy classification using convolutional neural networks, data augmentation and transfer learning: Application and a new public dataset

inferred← citeshttps://doi.org/10.1145/3673277.3673346openalex-snapshot

Research on the Digital Prediction Model of Cigarette Filament Feeding Process Based on BP Neural Network

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RDE-YOLOv7: An Improved Model Based on YOLOv7 for Better Performance in Detecting Dragon Fruits

inferred← citeshttps://doi.org/10.3390/electronics10202539openalex-snapshot

3D Face Recognition Based on an Attention Mechanism and Sparse Loss Function

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A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data

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