1. Structure of a semantic segmentation-based defect detection network for laser cladding infrared images
- Author
-
Shiyi Deng, Ruipeng Gao, Yiran Wang, Wei Mao, and Weikang Zheng
- Subjects
Applied Mathematics ,Instrumentation ,Engineering (miscellaneous) - Abstract
While selecting the most suitable infrared thermal imaging detection scheme for online inspection during laser cladding processing, this paper designs the RespathU-net semantic segmentation defect detection network for cladding coating defects in infrared images. The network is based on the U-net network framework. It is optimized and improved by redesigning the coding network structure, expanding the network perceptual field, and connecting the paths of residuals, thus enhancing the segmentation effect on the defective areas of the melt coating by addressing the problems that the original network cannot realize the end-to-end output and the poor segmentation effect on the complex objects. The generalization performance test and defect detection experiment of the RespathU-net network were conducted using the Kolektor SDD dataset and the infrared dataset constructed in this paper. The designed network is compared with fully convolutional networks, SegNet, U-net, and DeepLab_V3 in terms of average exchange ratio, similarity coefficient, and running time. The results show that the proposed RespathU-net achieves good multi-size feature recognition, and the effect is much better than other semantic segmentation networks. The via experimental results verify that the actual defect detection accuracy of the designed network is 87.01% .
- Published
- 2023