基于改进YOLOv8的复合材料夹杂缺陷检测研究

doi:10.19936/j.cnki.2096-8000.20250628.004

复合材料科学与工程 ›› 2025, Vol. 0 ›› Issue (6): 27-33.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

吴志成, 王明泉^*, 谢绍鹏, 路宇鹏, 曹振锋, 王晋华

中北大学信息与通信工程学院,太原 030051

收稿日期:2024-12-30 出版日期:2025-06-28 发布日期:2025-07-24
通讯作者: 王明泉(1970—),男,博士,教授,博士生导师,研究方向为图像处理与识别成像技术,wangmq＠nuc.edu.cn。
作者简介:吴志成(1997—),男,硕士研究生,研究方向为复合材料缺陷检测与深度学习。
基金资助:
国家自然科学基金(61171177)

Research on composite material inclusions defect detection based on improved YOLOv8

WU Zhicheng, WANG Mingquan^*, XIE Shaopeng, LU Yupeng, CAO Zhenfeng, WANG Jinhua

School of Information and Communication Engineering, North University of China, Taiyuan 030051, China

Received:2024-12-30 Online:2025-06-28 Published:2025-07-24

摘要/Abstract

摘要： 为解决复合材料夹杂缺陷检测中由于目标尺寸小和特征相似导致的检测精度不足问题,提出了一种改进的YOLOv8算法。首先,引入SPD卷积模块以减少信息损失,结合MCA注意力机制增强三维通道特征提取能力,从而提高缺陷识别精度。然后,通过BiFPN双向金字塔网络改进多尺度特征融合,提升模型对相似特征和尺寸差异缺陷的识别能力。最后,针对小目标检测的瓶颈,加入Shape IoU损失函数,通过优化边界框形态和尺度信息,提升小尺寸缺陷的检测性能。实验结果表明,改进算法在mAP@0.5和mAP@0.95上分别提升10.1%和7.4%,召回率提升8.1%。该方法在复合材料缺陷检测系统中的测试结果验证了其可靠性与实用性,为复合材料夹杂缺陷检测提供了一种高效、精准的技术方案。

关键词: 缺陷检测, 复合材料, YOLOv8

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 accuracy. 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, improving the detection performance for small-size defects. Experimental results show that the improved algorithm achieves 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.

Key words: defect detection, composite materials, YOLOv8

中图分类号:

TB332

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, 0(6): 27-33.

WU Zhicheng, WANG Mingquan, XIE Shaopeng, LU Yupeng, CAO Zhenfeng, WANG Jinhua. Research on composite material inclusions defect detection based on improved YOLOv8[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(6): 27-33.

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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

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