改进YOLOv5混合样本训练的绝缘子伞盘脱落缺陷检测方法

Improved YOLOv5 Mixed Sample Training for Detection of Insulator Umbrella Plate Falling Defects

为了实现输电线路巡检时绝缘子串及伞盘脱落缺陷的精准定位和识别,提出了一种基于改进YOLOv5混合样本训练的绝缘子缺陷检测模型。针对绝缘子缺陷图像稀少问题,提出了一种混合样本数据生成方法,通过将GrabCut算法与图像融合技术相结合实现数据集的扩充。针对绝缘子及缺陷的外形特点,利用长边定义法和环形平滑标签(circular smooth label,CSL)重新定义模型特征提取区域的坐标参数。通过增加角度信息,实现更加精确化的特征提取。通过将主干网络(Backbone)中部分特征层与路径聚合网络(path aggregation network,PAN)提取的特征相融合,对CSPDarkNet主干网络进行优化。改进后的YOLOv5 CSPDarkNet模型相较于改进前绝缘子缺陷检测精度提升了2.8个百分点,检测速率为20.5 FPS。实验结果表明,改进的绝缘子缺陷识别方法基本满足实际应用需求。

In order to realize the accurate location and identification of insulator string and umbrella plate falling defects during transmission line inspection,this paper proposes an insulator defect detection model based on improved YOLOv5 mixed sample training.Firstly,aiming at the scarcity of insulator defect images,a hybrid sample data generation method is proposed,which combines GrabCut algorithm with image fusion technology to expand the data set.Then,according to the shape characteristics of insulators and defects,the long edge definition method and CSL(circular smooth label)are used to redefine the coordinate parameters of the model feature extraction area.By adding angle information,more accurate feature extraction is realized.Finally,the CSPDarkNet backbone network is optimized by fusing some feature layers in the Backbone with the features extracted by PAN(path aggregation network).The improved YOLOv5 CSPDarkNet model increases the detection accuracy of insulator defects by 2.8 percentage points compared with the improved model,and the detection rate is 20.5 FPS.The experimental results show that the improved insulator defect identification method basically meets the needs of practical application.