Collaborative and Quantitative Prediction for Reinforcement and Penetration Depth of Weld Bead Based on Molten Pool Image and Deep Residual Network.
Jun LuYumin ShiLianfa BaiZhuang ZhaoJing HanPublished in: IEEE Access (2020)
Keyphrases
- weld bead
- image data
- image analysis
- image content
- single image
- template matching
- infrared
- image features
- input image
- image segmentation
- image representation
- multiscale
- image classification
- peer to peer
- image retrieval
- prediction accuracy
- test images
- feature points
- high resolution
- reinforcement learning
- image collections
- image pixels
- wireless sensor networks
- spatial information
- image matching
- region of interest