复合材料科学与工程Issue(6)：27-33,7.DOI:10.19936/j.cnki.2096-8000.20250628.004
基于改进YOLOv8的复合材料夹杂缺陷检测研究
Research on composite material inclusions defect detection based on improved YOLOv8
摘要
Abstract
In order to solve the problem of insufficient detection accuracy caused by small target size and similar features in the detection of composite inclusion defects,an improved YOLOv8 algorithm was proposed.First,the SPD convolution module is introduced to reduce information loss,and the MCA attention mechanism is incorporated to enhance the three-dimensional channel feature extraction capability,thereby improving defect recognition accura-cy.Subsequently,the BiFPN bidirectional pyramid network was used to improve the multi-scale feature fusion to improve the model's ability to identify similar features and size difference defects.Finally,to tackle the bottleneck of small target detection,a Shape IoU loss function is added to optimize the shape and scale of bounding boxes,im-proving the detection performance for small-size defects.Experimental results show that the improved algorithm a-chieves a 10.1％ increase in mAP@0.5 and a 7.4％ increase in mAP@0.95,with an 8.1％ improvement in recall rate.The test results in the composite material defect detection system validate the reliability and practicality of this method,providing an efficient and accurate technical solution for composite material inclusion defect detection.关键词
缺陷检测/复合材料/YOLOv8Key words
defect detection/composite materials/YOLOv8分类
通用工业技术引用本文复制引用
吴志成,王明泉,谢绍鹏,路宇鹏,曹振锋,王晋华..基于改进YOLOv8的复合材料夹杂缺陷检测研究[J].复合材料科学与工程,2025,(6):27-33,7.基金项目
国家自然科学基金(61171177) （61171177）
