Abstract: Provided are a pedestrian detection method and system for autonomous driving under complex backgrounds. The detection method includes: acquiring a dataset including pedestrian images under different backgrounds and different degrees of occlusion; constructing an Automatic Driving You Only Look Once Respond for Pedestrian Background Influence Object Detection Network (AD-YOLO-RPBNet) model, where a backbone part includes four serial structures including convolution blocks and diverse branch C3K2 modules; in a neck part of the AD-YOLO-RPBNet model, one diverse branch C3K2 module is introduced in a first column, three alternating diverse branch C3K2 modules are introduced in a second column, and an output of each diverse branch C3K2 module in the second column is connected to a detection head via a diverse efficient local attention (DELA) module; and training the model by using the obtained dataset, where the trained model is used for pedestrian detection of autonomous driving under complex backgrounds.

Abstract: A Feature-adaptive multi-scale You Only Look Once (FamsYOLO) network-based method and system for detecting surface defects of new vehicles are provided. The method includes: acquiring images of front, rear, left, right, and top surfaces of different types of new vehicles before delivery, marking vehicle surface defects in the images, and constructing an image dataset; constructing a FamsYOLO network, where a backbone part of the FamsYOLO network is serially formed by a head DBL-3 module and an intermediate residual structure from top to bottom, and a neck part of the FamsYOLO network includes, from bottom to top in sequence, a DBL-1 module, upsampling, a first feature concatenation, a FasterC3K2 module, Reshape transformation, a second feature concatenation, a FasterC3K2 module, and a Squeeze-and-Excitation Network (SENet) module; and training the FamsYOLO network using the image dataset, where the trained FamsYOLO network is used for detecting surface defects of new vehicles.

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.