改进YOLOv4算法的GFRP内部缺陷检测与识别

为实现航空玻璃纤维复合材料内部分层缺陷的智能识别,搭建了一种多自由度光纤耦合式太赫兹时域光谱系统,对带有模拟内部分层缺陷的样件进行检测,对检测结果图像进行了数据筛选、数据增强和数据标注,构建目标检测所用数据集.同时,提出了一种改进的YOLOv4算法,提高了缺陷智能识别的精度.实验结果表明,改进的YOLOv4算法在测试集得到91.05％ 的准确率和92.02％ 的召回率,分别较原YOLOv4算法提高了5.73％ 和8.51％,具有更强的特征提取能力,并展现出良好鲁棒性,明显消除了应用原YOLOv4算法的错检、漏检现象.

Detection and Recognition of GFRP Internal Defect Based on Modified YOLOv4 Algorithm

In order to realize the intelligent identification of internal lamination defects of aviation glass fiber composites, a spectroscopy system with multi degree of freedom fiber coupling terahertz time domain is built. The samples with simulated internal lamination defects are detected, and the detection results are filtered, enhanced and marked, and the data sets for target detection are constructed. At the same time, a modified YOLOv4 algorithm is proposed to improve the accuracy of intelligent defect recognition. The experimental results show that the improved YOLOv4 algorithm achieves 91.05% accuracy and 92.02% recall rate in the test set, which is 5.73% and 8.51% higher than the original YOLOv4 algorithm, respectively. This algorithm is characterized by a stronger feature extraction capability and good robustness, and obviously eliminates the error detection and omissions of the original YOLOv4 algorithm.