工艺与制造英文2024被引 4
基于改进YOLOv7的自动纤维铺放表面缺陷检测研究
Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
Liwei Wen, Shihao Li, Zhentao Dong, Haiqing Shen, Entao Xu · Nanjing University of Aeronautics and Astronautics
摘要整理
由于碳纤维预浸料束表面呈黑色高光泽特性，自动纤维铺放(AFP)工艺中表面缺陷的准确识别难度大。现有增强型YOLOv7算法在该检测任务中虽具有一定性能优势，但仍存在漏检、误检和置信度低等问题。本研究提出改进型YOLOv7算法，进一步提升AFP表面缺陷检测性能和泛化能力。首先，引入BiFormer注意力机制增强模型特征提取能力，使模型更关注小目标缺陷，提高特征判别性；其次，采用AFPN结构替代颈部PAFPN，强化特征融合，更好地保留语义信息，精细化整合多尺度特征；最后，以WIoU替代CIoU作为边界框回归损失函数，提高对小目标的敏感性，实现更精准的目标框预测，增强模型检测精度和泛化能力。通过一系列消融实验验证，改进型YOLOv7的mAP提升10.5%，帧率提高14 FPS，AFP工艺过程中最大检测速度达35 m/min，满足在线检测要求，可应用于AFP工艺表面缺陷检测。
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