基于机器视觉和深度学习神经网络的焊缝质量智能评价技术研究OA

Addressing the issues of strong subjectivity and low efficiency in traditional macro-level metallographic analysis,which relies on manual observation and experience-based judgment,a technology for intelligent evaluation of weld quality combining machine vision and deep learning neural networks is proposed.Through constructing a deep learning object de-tection model and a deep learning semantic segmentation model,automatic detection and identification of metallographic samples are realized.Experimental results show that the object detection model based on the Faster RCNN network has a recognition accuracy of up to 98.53%,and the semantic segmentation model based on the HRNetV2 network has an average intersection over union(IoU)of up to 96.72%.Deep learning models can learn complex features from a large amount of data,achieving intelligent evaluation of the macroscopic appearance and defects of welds.Compared with manual analysis,this technology reduces the analysis time per image from 30 seconds to 1 second,increasing detection efficiency by 5 times and reducing costs by 60%,meeting the requirements for batch detection,providing a new direction for the automated devel-opment of macro-level metallographic technology.