融合注意力机制的轻量级红外高压套管识别算法

为了提高红外图像中变压器高压套管的识别准确率，同时能够满足移动端和其他低端设备对目标检测网络的需要，本文提出一种改进的轻量级红外高压套管识别算法，采用Tiny YOLOv3目标检测网络作为基础检测网络。首先，通过融合CBAM（Convolutional Block Attention Module）注意力机制，将通道注意力与空间注意力机制串联，增大目标检测网络感受野，同时减轻网络计算任务，提升网络性能；然后，分别使用GIoU loss和Focal loss替代原有的边界框损失和置信度损失，从而提高对红外图像中的高压套管识别率，减少漏检、误检情况发生。实验结果表明，改进的网络相比于原Tiny YOLOv3网络，mAP提升到96.28%，F1提升到96.25%，权重文件大小为33.9 MB，远小于YOLOv3训练网络，能够更好地适用于低端设备，为智能变电站的在线监测提供了有利条件。

Lightweight Infrared HV Bushing Identification Algorithm Based on Attention Mechanism

In order to improve the identification accuracy of transformer HV bushing in infrared images and meet the needs of mobile terminal and other low-end devices for target detection network, this paper proposes an improved lightweight infrared HV bushing identification algorithm, using Tiny YOLOv3 target detection network as the basic detection network. First, through the fusion of the Convolutional Block Attention Module (CBAM) attention mechanism, the channel attention and the spatial attention mechanism are connected in series to increase the receptive field of the target detection network, while reducing network computing tasks and improving network performance. Then, GIoU loss and Focal loss are used to replace the original bounding box loss and confidence loss, thereby improving the recognition rate of the HV bushing in the infrared image and reducing the occurrence of missed and false detections. The experimental results show that compared with the original Tiny YOLOv3 network, the improved network increases mAP to 96.28%, increases F1 to 96.25%, and the weight size is 33.9 MB, less than that of YOLOv3 training network. It is better suitable for low-end equipment and provides favorable conditions for a smart substation online monitoring.