基于改进YOLO算法的光纤环绕制缺陷检测

作为光纤陀螺仪的核心部件，光纤环的绕制质量对光纤陀螺的精度至关重要。为了保证光纤环绕制的准确率和效率，提出了一种基于改进YOLO算法的目标检测方法。该方法采用Shufflenetv2网络来替代YOLO主干网络中的卷积层和池化层，提升了网络的特征提取能力；加入Focus模块提升训练速度；采用K-means聚类算法对原始锚框进行聚类，得到适用于光纤绕制缺陷的预测框，提高缺陷检测的准确率；同时修改损失函数，使用CIOU来计算定位损失，使用Focal Loss作为置信度损失和分类损失函数，加快网络收敛；并进行了数据增强，增强了网络的泛化能力。实验结果表明，改进后的YOLO算法的平均准确率达到了99.63%,相比于原始的YOLOv3-tiny算法提升了2.06%,检测速度达到91 fps,这将保证了光纤环的绕制系统的实际应用。

Research on defect detection system for FOC winding based on YOLO algorithm

As the core component of fiber optic gyroscope (FOG), the winding quality of the fiber optic coils (FOC) is critical to the accuracy of the FOG. In order to ensure the accuracy and efficiency of the fiber winding system, a defect detection method based on the improved YOLO algorithm is proposed. The model uses the Shufflenetv2 network to replace the convolution layer and pooling layer in the YOLO backbone network, which improves the feature extraction ability of the network; the Focus module is added to improve the training speed; the K-means clustering algorithm is used to cluster the original anchor boxes, and obtain a prediction frame suitable for fiber winding defect detection, the accuracy of defect detection is improved; at the same time, the loss function is modified, the CIOU is used to calculate the positioning loss, and the Focal Loss is used as the confidence loss and classification loss function to speed up the network convergence; and data enhancement is carried out to enhance the generalization ability of the network. It is concluded from the experiments that our proposed method is able to detect FOC winding defects with an average accuracy of 99.63%, which is an improvement of 2.06% over the original YOLO algorithm, and a detection speed of 91 fps. This will guarantee the practical application of the FOC winding system.