Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks.

Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks.

This paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by...

Full description

Bibliographic Details
Main Authors:	Oumayma Essid, Hamid Laga, Chafik Samir
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2018-01-01
Series:	PLoS ONE
Online Access:	http://europepmc.org/articles/PMC6226149?pdf=render

Similar Items

Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks
by: Shuai Wang, et al.
Published: (2021-02-01)

Deep Learning-Based Automatic Defect Detection of Additive Manufactured Stainless Steel
by: Md Hasib Zubayer, et al.
Published: (2023-12-01)

A deep region-based pyramid neural network for automatic detection and multi-classification of various surface defects of aluminum alloys
by: Chen, Keyu, et al.
Published: (2022)

Automatic Modulation Classification with Deep Neural Networks
by: Clayton A. Harper, et al.
Published: (2023-09-01)

Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
by: Luigi Nele, et al.
Published: (2022-04-01)

Feature-based volumetric defect classification in metal additive manufacturing
by: Arun Poudel, et al.
Published: (2022-10-01)

Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks
by: Limei Song, et al.
Published: (2018-10-01)

Automatic Fruits Classification System Based on Deep Neural Network
by: Khadija Munir, et al.
Published: (2020-12-01)

Automatic Leukocytes Classification using Deep Convolutional Neural Network
by: SUGONDO HADIYOSO, et al.
Published: (2023-01-01)

Research and Evaluation on an Optical Automatic Detection System for the Defects of the Manufactured Paper Cups
by: Ping Wang, et al.
Published: (2023-01-01)

Automatic Bounding Box Annotation with Small Training Datasets for Industrial Manufacturing
by: Manuela Geiß, et al.
Published: (2023-02-01)

Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks
by: Po-Chou Shih, et al.
Published: (2020-05-01)

Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks
by: Khalil Aljohani, et al.
Published: (2022-06-01)

Ensemble Deep Neural Network for Automatic Classification of EEG Independent Components
by: Fabio Lopes, et al.
Published: (2022-01-01)

Automatic classification of cowpea leaves using deep convolutional neural network
by: Vijaya Choudhary, et al.
Published: (2023-08-01)

Automatic Modulation Classification: Convolutional Deep Learning Neural Networks Approaches
by: Hany S. Hussein, et al.
Published: (2023-01-01)

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing
by: Reenu Mohandas, et al.
Published: (2024-01-01)

Deep Transfer Learning for Macroscale Defect Detection in Semiconductor Manufacturing
by: Waterworth, John Timothy
Published: (2023)

Steel Surface Defect Classification Using Deep Residual Neural Network
by: Ihor Konovalenko, et al.
Published: (2020-06-01)

Pavement defect detection with deep convolutional neural network
by: Chan, Zhen Yi
Published: (2023)

Automatic Detection and Classification of Defective Areas on Metal Parts by Using Adaptive Fusion of Faster R-CNN and Shape From Shading
by: Feyza Selamet, et al.
Published: (2022-01-01)

Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets
by: Renato Bruni, et al.
Published: (2023-01-01)

Automatic Detection and Classification of Cough Events Based on Deep Learning
by: Hossein Tabatabaei Seyed Amir, et al.
Published: (2020-09-01)

A Unique Identification-Oriented Black-Box Watermarking Scheme for Deep Classification Neural Networks
by: Mouke Mo, et al.
Published: (2024-03-01)

Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification
by: Kazuki Koga, et al.
Published: (2022-04-01)

Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images
by: Junjie Wang, et al.
Published: (2022-12-01)

Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
by: Jiawei Zhang, et al.
Published: (2023-04-01)

Deep Learning for Automatic Defect Detection in PV Modules Using Electroluminescence Images
by: Fatma Mazen Ali Mazen, et al.
Published: (2023-01-01)

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network
by: Xiaoming Lv, et al.
Published: (2020-03-01)

Automatic multiclass classification of laryngeal cancer using deep convolution neural networks
by: Ramesh Munirathinam, et al.
Published: (2024-01-01)

Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
by: Ke Zang, et al.
Published: (2021-09-01)

A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing
by: Huilin Zheng, et al.
Published: (2021-10-01)

Automatic Detection of Ballast Unevenness Using Deep Neural Network
by: Piotr Bojarczak, et al.
Published: (2024-03-01)

Automatic Detection of Track Length Defects
by: Kanis Juaraj, et al.
Published: (2022-01-01)

Automatic Defect Detection of Pavement Diseases
by: Langyue Zhao, et al.
Published: (2022-09-01)

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification
by: Nesamony Sajitha, et al.
Published: (2024-04-01)

On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning
by: Mohammad Fraiwan, et al.
Published: (2022-06-01)

Defect Classification for Additive Manufacturing with Machine Learning
by: Mika León Altmann, et al.
Published: (2023-09-01)

Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data
by: Qiang Fang, et al.
Published: (2023-05-01)

Automated Fabric Defect Detection and Classification: A Deep Learning Approach
by: NC Sandhya, et al.
Published: (2021-12-01)