郑州大学学报（工学版）2024，Vol.45Issue(1)：122-128,7.DOI:10.13705/j.issn.1671-6833.2024.01.004
基于YOLOv5s和Android部署的电气设备识别
Electrical Equipment Identification Based on YOLOv5s and Android Deployment
摘要
Abstract
Aiming at the requirement of real-time detection of various electrical equipment in substation,an electrical equipment identification method based on improved YOLOv5s was proposed,and an electrical equipment identification APP based on Android was designed to recognize and learn electrical equipment.Six common electrical equipments of substation,such as power transformer and insulator string,were taken as examples to construct image data set.After image preprocessing of data set,YOLOv5s algorithm was improved,introducing C2f module to improve the detection accuracy of small targets,and using Soft-NMS to improve the screening ability of detection frame,so as to reduce the phenomenon of missing and false detection.The improved algorithm was used to train the model of data set.The trained identification network model was deployed through the TensorFlow Lite framework,and the electrical equipment identification APP was designed.It was verified that the mAP value of the improved substation electrical equipment identification network model was stable at 91.6％,which was 3.3 percent-age points higher than that of the original model.After deployment,the APP had the interface of equipment recog-nition and equipment introduction,and the recognition time of each image was less than 1 s when using mobile ter-minal,which had a fast recognition speed and high recognition accuracy,and could effectively realize the real-time detection and equipment learning of electrical equipment in substation.关键词
电气设备/改进YOLOv5s/Android/TensorFlow Lite/图像识别Key words
electrical equipment/improved of YOLOv5s/Android/TensorFlow Lite/image identification分类
信息技术与安全科学引用本文复制引用
廖晓辉,谢子晨,路铭硕..基于YOLOv5s和Android部署的电气设备识别[J].郑州大学学报（工学版）,2024,45(1):122-128,7.基金项目
河南省自然科学基金资助项目(232300421198) （232300421198）
河南省科技攻关计划项目(222102220053) （222102220053）
