基于YOLO v3的高压开关设备异常发热点目标检测方法

The Target Detection Method for Abnormal Heating Point of High-Voltage Switchgear Based on YOLO v3

针对高压开关设备红外图像异常发热点检测中存在目标位置场景复杂和大小不均衡带来的检测准确率下降问题,基于YOLO v3算法,通过添加卷积模块及调整部分超参数对其基础网络架构进行优化,以实现高压开关设备异常发热点的快速检测、识别和定位。同时,建立了用于高压开关设备红外图像异常发热点的数据集,通过训练得出合适的权重。实验结果表明,该检测方法识别速度快,准确率高且具有较强的泛化能力,测试准确率达到91.83%,可将其初步应用于高压开关设备异常发热点目标检测中。

This study aims to solve the problem of reduced detection accuracy caused by a complex target-position scene and an uneven size in the detection of the abnormal heating point in an infrared image of a high-voltage switchgear.According to the YOLO v3 algorithm,the basic network architecture was optimized by including a convolution module and adjusting some hyper-parameters to realize rapid detection and identification of abnormal heating points in high-voltage switchgears.Simultaneously,a dataset for abnormal heating points of infrared images in high-voltage switchgears was established,and appropriate weights were obtained through training.The experimental results indicated that the detection method had a fast recognition speed,high accuracy,and strong generalization ability.The test accuracy reached 91.83%,indicating that the method can be initially applied to the detection of abnormal heating-point targets in high-voltage switchgears.