D
Domen Tabernik
Researcher at University of Ljubljana
Publications - 21
Citations - 853
Domen Tabernik is an academic researcher from University of Ljubljana. The author has contributed to research in topics: Convolutional neural network & Object detection. The author has an hindex of 7, co-authored 21 publications receiving 346 citations. Previous affiliations of Domen Tabernik include University of Birmingham.
Papers
More filters
Journal ArticleDOI
Segmentation-based deep-learning approach for surface-defect detection
TL;DR: A segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
Journal ArticleDOI
Deep Learning for Large-Scale Traffic-Sign Detection and Recognition
Domen Tabernik,Danijel Skočaj +1 more
TL;DR: A convolutional neural network approach, the mask R-CNN, is adopted to address the full pipeline of detection and recognition with automatic end-to-end learning, which is sufficient for deployment in practical applications of the traffic-sign inventory management.
Journal ArticleDOI
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
TL;DR: In this article, a segmentation-based deep learning architecture is proposed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection.
Journal ArticleDOI
Mixed supervision for surface-defect detection: From weakly to fully supervised learning
TL;DR: In this article, a deep learning architecture for surface-defect detection in industrial quality control has been proposed, which is composed of two sub-networks yielding defect segmentation and classification results.
Posted Content
End-to-end training of a two-stage neural network for defect detection.
TL;DR: End-to-end training of the two-stage neural network is introduced together with several extensions to the training process, which reduce the amount of training time and improve the results on the surface defect detection tasks.