Wanrun Li^1,2,3,*, Wenhai Zhao¹, Tongtong Wang¹, Yongfeng Du^1,2,3

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 553-575, 2024, DOI:10.32604/sdhm.2024.050751 - 19 July 2024

Abstract The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage, impacting the aerodynamic performance of the blades. To address the challenge of detecting and quantifying surface defects on wind turbine blades, a blade surface defect detection and quantification method based on an improved Deeplabv3+ deep learning model is proposed. Firstly, an improved method for wind turbine blade surface defect detection, utilizing Mobilenetv2 as the backbone feature extraction network, is proposed based on an original Deeplabv3+ deep learning model to address the issue of limited robustness. Secondly, through integrating the concept of… More > Graphic Abstract

Pierre-Frédéric Villard^1,*, Maureen Boudart², Ioana Ilea³, Fabien Pierre¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1639-1648, 2022, DOI:10.32604/fdmp.2022.021726 - 27 June 2022

Abstract Industrial woven meshes are composed of metal materials and are often used in construction, industrial and residential activities or applications. The objective of this work is defect detection in industrial fabrics in the quality control stage. In order to overcome the limitations of manual methods, which are often tedious and time-consuming, we propose a strategy that can automatically detect defects in micrometric steel meshes by means of a Convolutional Neural Network. The database used for such a purpose comes from real problem data for anomaly detection in micrometric woven meshes. This detection is performed through More >

Wuqin Tang, Qiang Yang, Wenjun Yan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1423-1439, 2022, DOI:10.32604/cmes.2022.018313 - 30 December 2021

Abstract Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed… More >