一种基于YOLOv3算法的3D打印点阵结构缺陷识别方法

针对3D打印点阵结构中缺陷目标因尺寸小、缺陷特征微弱而难以准确自动识别的问题,提出了一种基于YOLOv3算法的点阵结构缺陷智能识别新方法.该方法利用深度学习网络模型在特征提取方面的优势,采用多尺度网络进行预测,将缺陷的分类和定位问题作为回归问题处理.实验结果表明,所提算法实现了一种3D打印点阵结构内部典型缺陷的识别,缺...

A Method for Detecting Internal Defects of Metal Lattice Structure Based on YOLOv3 Algorithm

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.