基于YOLOv5算法的无人机电力巡检快速图像识别

Fast Image Recognition of UAV Power Inspection Based on YOLOv5 Algorithm

针对图像识别算法硬件资源消耗大、识别速度慢的问题,基于YOLOv5算法设计了专用于电力巡检无人机的绝缘子目标检测模型。对算法中卷积操作模块和残差模块进行了改进,通过增加卷积层数来加深算法的学习深度。为了提高训练速度,采用多次循环神经网络训练法实现了对数据集的学习训练。模型的单张图片识别速度最快为0.061 s,绝缘子识别精度最高达到98.9%。结果表明,在消耗较少硬件计算资源的前提下,该模型可以直接对航拍采集到的图像进行处理,实现快速识别,可以满足电力无人机巡检过程中图像实时处理的要求。

Aiming at the problems of large hardware resource consumption and slow recognition speed of image recognition algorithm,based on YOLOv5 algorithm,an insulator target detection model dedicated to power inspection UAV is designed.The convolution operation module and residual module in the algorithm are improved,and the learning depth of the algorithm is deepened by increasing the number of convolution layers.In order to improve the training speed,the learning and training of the data set is realized by using the multiple recurrent neural network training method.The fastest single image recognition speed of the model is 0.061 s,and the highest recognition accuracy of insulators is 98.9%.The results show that under the premise of consuming less hardware computing resources,the model can directly process the images collected by aerial photography,realize rapid identification,and can meet the requirements of real-time image processing in the process of power UAV inspection.