The Research And Implementation Of Welding Defect Object Detection Based On Faster R-CNN Model

Non-destructive testing(NDT)technology is an important method for welding quality inspection and is used in aerospace,construction,pipeline transportation and other fields.The defects produced in the welding process have a crucial impact on the quality and service life of the equipment.Detecting whether there are any defects in the welding object,can not only verify whether the quality of the weld of the tested target has reached the welding standards,but also find destructive defects in time to repair them and improve the quality of the welding.The traditional and commonly method of welding detection is to artificially detect the X-ray images after the contrast and grayscale transformation using special software equipment by the NDT inspectors.This process is not only inefficient,but also greatly affected by human factors.In view of this,it is extremely important and practical to explore a method that can automatically locate and classify potential welding defects in welding images.By understanding the current research status of welding defect detection at home and abroad,and the current development of deep learning,especially the research progress in the field of object detection,the research direction in this paper is determined.With the help of the classic convolutional neural network model in the field of deep learning,the Faster RCNN model,the steps of the original welding defect detection are simplified and the automatic detection of welding defects is researched.The images in the data set used in the experiment are digital pictures obtained from the actual X-ray film collected from various places and converted by a special film scanning equipment.The adaptive histogram equalization and two median blur were used to perform image enhancement and denoising on the extracted welding area.In the model training process,this paper attempts to optimize the model by methods such as feature enhancement,label smoothing and other tricks to improve the detection performance.Based on the research results,this paper finally realized a system that can automatically detect the types of defect in the picture and the coordinates of the defect area.This is a completely different attempt from the traditional methods.Deep learning is combined with specific applications in industry,allowing the model to train and learn from the data set to achieve the performance of automatic detection.This will be hot directions and trends in the field of non-destructive testing in the future.