Abstract Machine vision significantly improves the efficiency, quality, and reliability of defect
detection. In visual inspection, excellent optical illumination platforms and suitable image …

Due to the advances in the digitalization process of the manufacturing industry and the
resulting available data, there is tremendous progress and large interest in integrating …

Automated surface inspection (ASI) is a challenging task in industry, as collecting training
dataset is usually costly and related methods are highly dataset-dependent. In this paper, a …

This study proposed a scheme based on Machine Learning (ML) models to detect geometric
defects of additively manufactured objects. The ML models are trained with synthetic 3D …

In order to effectively identify and classify weld defects of thin-walled metal canisters, a weld
defect detection and classification algorithm based on machine vision is proposed in this …

Non-destructive testing of welds based on the radiographic image is crucial for improving
the reliability of aerospace structural components. The deep learning method represented …

Pipeline safety is closely related to people's lives, environment and economic development.
The traditional methods for pipeline inspection are laborious and very expensive, such as …

In order to remove the limitations of human interpretation, many computer-aided algorithms
have been developed to automatically detect defects in radiographic images. Compared …

This paper presents a method for the automatic detection and classification of defects in
radiographic images of welded joints obtained by exposure technique of double wall double …

Traditional Image Processing Techniques (IPT), used for automating the detection and
classification of weld defects from radiography images, have their own limitations, which can …