activity
20192025
most citedSegmentation-Based Deep-Learning Approach for Surface-Defect Detection

869 citations · 1.3k across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV202413 cited

Dense Center-Direction Regression for Object Counting and Localization with Point Supervision

Domen Tabernik, Jon Muhovič, Danijel Skočaj

Object counting and localization problems are commonly addressed with point supervised learning, which allows the use of less labor-intensive point annotations. However, learning b…

cs.CV202413 cited

Center Direction Network for Grasping Point Localization on Cloths

Domen Tabernik, Jon Muhovič, Matej Urbas +1

Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, p…

cs.CV2021359 cited

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

Jakob Božič, Domen Tabernik, Danijel Skočaj

Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data need…

cs.CV2020

End-to-end training of a two-stage neural network for defect detection

Jakob Božič, Domen Tabernik, Danijel Skočaj

Segmentation-based, two-stage neural network has shown excellent results in the surface defect detection, enabling the network to learn from a relatively small number of samples. I…

cs.CV2019

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

Domen Tabernik, Danijel Skočaj

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign in…

cs.CV2019869 cited

Segmentation-Based Deep-Learning Approach for Surface-Defect Detection

Domen Tabernik, Samo Šela, Jure Skvarč +1

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of…