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【摘 要】
：
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,缺陷检测回召率为96.6％,准确率为93.2％,模型平均精度均值为0.957,为进一步精确表征缺陷并分析缺陷对点阵结构性能的影响提供了依据.
【机 构】
：
燕山大学电气工程学院,河北秦皇岛066004;燕山大学测试计量技术及仪器河北省重点实验室,河北秦皇岛066004
【出 处】
：
计量学报
【发表日期】
：
2022年1期
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