Mixed supervision for surface-defect detection: From weakly to fully supervised learning

A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies

(2020) Gaokai Liu et al.   SENSORS

A Compact Convolutional Neural Network for Surface Defect Inspection

(2020) Yibin Huang et al.   SENSORS

Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection

(2020) Ce Ge et al.   COMPUTERS IN INDUSTRY

Reconstruction by inpainting for visual anomaly detection

(2020) Vitjan Zavrtanik et al.   PATTERN RECOGNITION

A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification

(2020) Yang Liu et al.   Materials

An Efficient Network for Surface Defect Detection

(2020) Zesheng Lin et al.   Applied Sciences-Basel

A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery

(2020) Yatao Yang et al.   COMPUTERS IN INDUSTRY

CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection

(2020) Jiabin Zhang et al.   PATTERN RECOGNITION

Stable and explainable deep learning damage prediction for prismatic cantilever steel beam

(2020) Darian M. Onchis et al.   COMPUTERS IN INDUSTRY

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

(2019) Thomas Schlegl et al.   MEDICAL IMAGE ANALYSIS

Stacked convolutional sparse denoising auto-encoder for identification of defect patterns in semiconductor wafer map

(2019) Jianbo Yu et al.   COMPUTERS IN INDUSTRY

A fast and robust convolutional neural network-based defect detection model in product quality control

(2017) Tian Wang et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

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

(2016) Daniel Weimer et al.   CIRP ANNALS-MANUFACTURING TECHNOLOGY