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

By Tian Wang, Yang Chen, Meina Qiao, Hichem Snoussi. The International Journal of Advanced Manufacturing Technology, 2017

442 citations recorded by the source corpus.

How researchers see this paper

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

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks (2018, methodology): “For the FCN method [32], it can achieve good detection effects for most of the defects.”
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges (2020, background): “These defects increase the costs incurred by enterprises, shorten the service life of manufactured products, and result in an extensive waste of resources, thereby causing substantial harm to people and their safety [7].”
Mixed supervision for surface-defect detection: from weakly to fully supervised learning (2021, unknown): “The proposed method is capable of real-time detection, achieving 57 fps on 512 × 512 images (DAGM), 23 fps on 512 × 1408 images (KSDD), 85 fps on 232 × 640 images (KSDD2) and 36 fps on 1600 × 256 images (Severstal Steel), running on a single Nvidia RTX2080Ti GPU.”
Machine learning-based image processing for on-line defect recognition in additive manufacturing (2019, methodology): “Xianghua Xie Image processing-based surface defect detection in industrial applications demands great real-time performance, according to previous research[4].”