Research on automatic detection of composite inclusion defects based on Mask R-CNN

doi:10.19936/j.cnki.2096-8000.20240128.011

Abstract

Abstract: In order to improve the detection efficiency of composite inclusion defects, an automatic inclusion defect detection system based on deep learning network is proposed in this paper. In the process of image preprocessing, a two-stage unsharping mask algorithm is used to highlight the features of inclusion defects, and a composite image database of inclusion defects is constructed. The Mask R-CNN network model is used, and the optimal weight parameters are obtained through network model training. Finally, the defect detection software system is designed and realized. The experimental results show that the network accuracy of Mask R-CNN algorithm is 94.6%, the recall rate is 92.4%, and the AP value is 87.3%. The system is convenient and fast in application, and will effectively improve the efficiency and accuracy of defect detection for front-line personnel.

Key words: unsharp mask, image processing, deep learning, defect detection, system design, composites

CLC Number:

TB332

LI Leilei, WANG Mingquan, ZHAO Fubao, ZHU Huanyu, FENG Xiaoyu, XIE Shaopeng. Research on automatic detection of composite inclusion defects based on Mask R-CNN[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(1): 83-88.

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