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

03/20/2019
by   Domen Tabernik, et al.
16

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 visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents 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. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the domain of a surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrate that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25-30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.

READ FULL TEXT

page 5

page 6

page 7

page 9

page 10

page 12

page 13

page 14

research
11/09/2020

Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks

Surface anomaly detection plays an important quality control role in man...
research
11/16/2018

Anomaly Detection using Deep Learning based Image Completion

Automated surface inspection is an important task in many manufacturing ...
research
03/20/2023

Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking

The huge supporting training data on the Internet has been a key factor ...
research
12/02/2016

Cognitive Deep Machine Can Train Itself

Machine learning is making substantial progress in diverse applications....
research
11/24/2019

AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces

Humans can easily detect a defect (anomaly) because it is different or s...
research
12/12/2020

Computer Vision and Normalizing Flow Based Defect Detection

Surface defect detection is essential and necessary for controlling the ...
research
04/17/2019

An Online Learning Approach for Dengue Fever Classification

This paper introduces a novel approach for dengue fever classification b...

Please sign up or login with your details

Forgot password? Click here to reset