Defect Detection and Classification Algorithm of Metal Nanomaterials Based on Deep Learning

Abstract

With the rapid development of nanotechnology, researchers can prepare nanomaterials by various methods. The rapid development of nanotechnology has greatly promoted the wide application of nano materials in the field of detection and catalysis. Especially the metal nanomolecules have good biocompatibility, absorption of plasma surface resonance, enhanced Raman surface and other properties. Metal nanomolecules have good biocompatibility, absorption of plasma surface resonance and enhanced Raman surface Rice inspection and catalytic application have attracted wide attention. This paper mainly studies the defect detection and classification algorithm of metal nanomaterials based on deep learning. Through the experimental phenomena, we can understand and master the defect detection methods of metal nanomaterials, and review the problems and future development direction of the preparation of metal nanomaterials. Deep learning algorithm the first mock exam deep learning network model, metal defect multi-mode detection method and the classification of metal surface defects are investigated. The defect detection of metal nano materials based on deep learning is realized. The whole defect and the quantitative detection of metal defects are realized. The detection range of traditional single mode nondestructive testing technology is overcome. The deficiency of accurate quantitative detection is difficult. The results show that there are five characteristic parameters for metal nano surface defect detection. In the deep learning based defect detection and classification algorithm, the big data technology is used to analyze the complete defect data, environmental data and working intensity data to complete the prediction of the future development trend of defects, which can play an important role in the maintenance of materials It is also of great significance to the development of metal nano detection technology.