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复杂背景条件下的电气设备图像实例分割算法

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变电站巡检拍摄的电气设备可见光图像存在背景杂乱、目标轮廓不规则等特点,造成设备分割精度不高,影响智能巡检系统设备识别效果.基于此,提出一种改进的YOLACT++模型,实现设备目标精确实例分割.首先,设计了电气设备特征提取主干网络DAGNet,提升了网络对复杂背景下重要特征的关注度;同时在原型网络分支引入3D注意力模块SimAM,降低混乱背景对目标分割的干扰.使用某市8个区域58座110 kV变电站和86座35 kV变电站巡检所得避雷器、断路器等6类电气设备的1 730张可见光图像的标记数据集对该模型进行验证,实验结果表明,改进YOLACT+ +模型分割的APall指标为84.1%,相较原模型提高了4.4%,与YOLACT、Mask R-CNN和YOLOv8模型相比分别高出4.0%、9.3%、1.6%,较好地实现了6类电气设备的识别,可满足电力巡检中准确性和快速性的要求.
Instance segmentation algorithm for electrical equipment images under complex background conditions
The visible light images of electrical equipment in substation inspection are characterized by background clutter and irregular target contours,causing poor equipment segmentation accuracy and affecting the equipment recognition effect of intelligent inspection systems.This paper proposes an improved YOLACT+ + model to realize accurate instance segmentation of equipment targets.First,the electrical equipment feature extraction backbone network DAGNet is designed to improve the network's attention to important features in the complex background.Simultaneously,the 3D attention module SimAM is introduced in the prototype network branch to reduce the interference of the chaotic background on target segmentation.The model is validated using a labeled dataset of 1 730 visible images of six types of electrical equipment,including surge arresters and circuit breakers,obtained from inspections of 58 110 kV substations and 86 35 kV substations in eight regions of a city.The experimental results show that the APall index of the improved YOLACT+ + model segmentation is 84.1%.It is 4.4%higher,and with YOLACT,Mask R-CNN,and YOLOv8 models,it is 4.0%,9.3%,and 1.6%higher,better realizing the recognition of the six types of electrical equipment.The accuracy and rapidity of electric power inspection are met.

electrical equipment detectioninstance segmentationYOLACT++visible image

张志君、张惊雷、贾鑫

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天津理工大学电气工程与自动化学院 天津 300384

天津市复杂系统控制理论及应用重点实验室 天津 300384

天津理工大学工程训练中心 天津 300384

电气设备识别 实例分割 YOLACT++ 可见光图像

国家自然科学基金青年项目

62302335

2024

电子测量技术
北京无线电技术研究所

电子测量技术

CSTPCD北大核心
影响因子:1.166
ISSN:1002-7300
年,卷(期):2024.47(1)
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