Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

By Daniel Weimer, Bernd Scholz-Reiter, Moshe Shpitalni. 2016

530 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/581f5bf822e701d3dfa80dbb82c5a3ac7633791f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Nan Chen
2. Paolo Dario
3. Weiwen Peng
4. Jun Zhu
5. Stefano Roccella
6. Liquan Zhao
7. Shuaiyang Li

In Industrial and Manufacturing Engineering

1. Mario Milazzo
2. Nan Chen
3. Paolo Dario
4. Marcello Chiurazzi
5. Tamás Czimmermann
6. Calogero Maria Oddo
7. Hichem Snoussi
8. Weiwen Peng

In Mechanical Engineering

1. Nan Chen
2. Calogero Maria Oddo
3. Weiwen Peng
4. Jun Zhu
5. Liquan Zhao
6. Shuaiyang Li

Citations in the corpus

• Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, methodology): “[26] performed a visual defect inspection by studying various design configurations of a deep CNN.”
• Mixed supervision for surface-defect detection: from weakly to fully supervised learning (2021, unknown): “0 paradigm, deep-learningbased algorithms have started being employed [24, 41, 35, 42, 17, 40].”
• A fast and robust convolutional neural network-based defect detection model in product quality control (2017, methodology): “For defect detecion, the paper [15] proposed a novel deep CNN architecture to detect defects, which taked all types of defect free and defective samples together as the input.”
• Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020, methodology): “Thus, CNN-based architectures are widely applied to automatic feature extraction [103] as well as on image defect detection [104]–[108] in industrial inspection.”
• Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, background): “Within the past years, a number of relevant studies have been published on defect detection solutions using deep learning [175,210–214].”
• Object Detection Algorithm Based on Improved YOLOv3 (2020, methodology): “It has been widely used in autonomous driving [2], pedestrian detection [3], medical imaging [4], industrial detection [5], robot vision [6], intelligent video surveillance [7], remote sensing images [8], etc.”
• Segmentation-based deep-learning approach for surface-defect detection (2019, unknown): “As opposed to some related works (Ra ˇ cki et al 2018; Weimer et al 2016), the proposed network is applied to real-world examples instead of us-ing synthetic ones.”