改进的YOLOv3-Tiny网络在风机叶片损伤检测中的应用

为了解决YOLOv3-Tiny对无人机采集的风机叶片图像损伤检测精度不高的问题,提出一种基于深度学习的风机叶片图像损伤检测方法。首先提出一种跨越式特征联合网络结构,由卷积层和拼接层构成,将不同深度的特征信息进行融合再学习,提取目标多层级特征信息;其次引入Inception模块结构,其中4个平行通道的多个卷积核对输入的特征图进行组合和压缩,在减少网络的学习参数的同时更好地表征图像特征信息,提高小目标的检测精度。实验表明,改进后算法的检测精度提高了2.69%,在自制的数据集中mAP可以达到88.58%,并且模型的参数缩小了4倍。因此,改进的方法比传统的YOLOv3-Tiny网络具有更好的检测效果。研究结果可为基于图像的损伤检测和风机叶片损伤智能识别提供参考。

Application of improved YOLOv3-Tiny network in fan blade damage detection

In order to solve the low precision of damage detection using YOLOv3-Tiny network for UAV collected fan blade images,a deep learning-based damage detection algorithm was proposed.Firstly,a leaping feature joint network structure was proposed,which was composed of a convolutional layer and a splicing layer.The feature information of different depths was fused and learned to extract the information of the target multi-level feature.Secondly,the inception module structure was introduced,and the input feature maps were compressed by multiple convolution kernels with 4 parallel channels,which reduced the learning parameters of the network and better characterized the image feature information.Then the detection accuracy of small targets was improved.Experiments show that the detection accuracy of the improved algorithm is increased by 2.69%,the mAP reaches 88.58% in the self-made dataset,and the parameters of the model are reduced by 4 times.The detect precision of this method is better than that of YOLOv3-Tiny network in the task of wind turbine blade damage detection.The results provide reference for image-based damage detection and intelligent identification work of fan blade damage.