Abstract: Provided are a quantification method for microscopic cracks inside 3D printed concrete and a system thereof. Before measuring the microscopic crack images according to relevant standards, firstly, a denoising network, improved attention-guided denoising neural network (IADNet) is adopted. IADNet can extract features from microscopic crack images from different perspectives, perceive noise from multiple levels, and perform denoising processing, greatly improving image quality and enhancing texture details, which is beneficial for training segmentation networks. The combination of IADNet and semantic segmentation algorithm has the ability to finely recognize image information, quantify microscopic crack recognition, overcome the shortcomings of measurement and analysis of microscopic cracks inside 3D printed concrete, and improve construction efficiency and quality.

Abstract: The present disclosure relates to a method and system for testing mechanical tolerance of a photovoltaic module. The method include sequential steps of: according to a predetermined appearance inspection standard, performing an appearance defect inspection on the photovoltaic module; testing a light attenuation rate of the photovoltaic module, and when the light attenuation rate is smaller than a predetermined attenuation threshold, under a predetermined test condition, checking an output power of the photovoltaic module; performing a mechanical tolerance test on the photovoltaic module; according to the appearance inspection standard, performing an appearance defect inspection on the photovoltaic module; checking the output power of the photovoltaic module under the predetermined test condition; and ending testing of the mechanical tolerance.

Abstract: The present disclosure relates to a method and system for testing weather resistance of a photovoltaic module.