A Method for Detecting Internal Defects of Metal Lattice Structure Based on YOLOv3 Algorithm
ZHANG Yu-yan1,2, REN Teng-fei1,2, WEN Yin-tang1,2
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Hebei Province Key Laboratory of Measuring and Testing Technologies and Instruments,Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：To solve the problem that the defect in the lattice structure is difficult to accurately identify due to the small size and weak feature,an intelligent defect recognition method based on YOLOv3 algorithm is proposed.This method takes advantage of the deep learning network model in feature extraction,uses a multi-scale network to predict and treats the classification and location of defects as regression problems.The proposed algorithm realizes the identification of internal defects in a 3D printed lattice structure.And the detect recall is 96.6%,the accuracy is 93.2%,and the mean average precision value of model is 0.957.It provides a basis for further accurate characterization of defects and analysis of the effects of defects on the performance of lattice structures.
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2022, Vol. 43 
