基于改进YOLOv5的绝缘子快速检测

Fast Detection of Insulators Based on Improved YOLOv5

为了解决复杂背景下,绝缘子准确快速识别的实时性问题,提出了一种基于YOLOv5改进的轻量型绝缘子检测算法模型。在网络结构中融入了Shufflenet v2网络和深度卷积模块,通过控制通道数和减少网络层数来减少参数量,采用K-means算法调整anchor框,并提出了改进损失函数DCIoU加速了损失函数的收敛。实验结果表明,改进的YOLOv5算法在参数量上仅有原网络的10%,准确率提高了0.2%,推理速度提升了2帧。

In order to solve the real-time problem of accurate and rapid identification of insulators under the complex background,an improved lightweight insulator detection algorithm model based on YOLOv5 is proposed.Shufflenet v2 network and deep convolution modules are integrated into the network structure to reduce the number of parameters by controlling the number of channels and reducing the number of network layers.K-means algorithm is adopted to adjust the anchor box,and the improved loss function DCIoU is proposed to accelerate the convergence of the loss function.Experimental results show that the improved YOLOv5 algorithm is only 10%of the original network in terms of the number of parameters,the accuracy rate is increased by 0.2%,and the inference speed is increased by 2 frames.