基于改进YOLO v5方法的电力设备红外图像识别方法

Infrared Image Recognition Method for Power Equipment Based on Improved YOLO v5

为解决电力设备红外图像有遮挡、分类不准确和特征提取不充分等问题,本文提出一种改进的YOLOv5识别方法。首先通过迁移学习的方法,将电力设备可见光图像和红外图像相融合,接着将Triplet注意力机制嵌入到特征提取网络中,对关键特征信息进行加权强化,最后通过多尺度融合的方法实现不同目标的识别。研究结果表明:相对于Faster R-CNN和SSD,本文方法的识别精度和识别效率最高,且适应于复杂背景下的多类型电力设备识别;本文方法的模型仅4.1 MB,相较于SSD缩减了80.8%,实现了网络模型的轻量化。本文方法为电力设备红外图像智能检测提供了新颖可行的方案。

This study proposes an improved YOLO v5 method to solve the problems of inaccurate classification and insufficient feature extraction from power equipment infrared images.First,the visible light data and infrared images of the power equipment were fused using the transfer learning method.The triplet attention mechanism was then embedded into the feature extraction network for weighted intensification of key feature information.Finally,different targets were identified using multiscale fusion.The results show that compared with faster R-CNN and SSD,the proposed method has higher recognition accuracy and efficiency and is suitable for image recognition of multi-type power equipment in complex backgrounds.This method realizes a lightweight network model with a size of only 4.1 MB,which is a reduction of 80.8%compared to that of SSD,providing a novel and feasible scheme for intelligent infrared image detection of power equipment.