Electrical Equipment Identification Based on YOLOv5s and Android Deployment

NAVIGATE

Table of Contents

STATISTICS

Viewed644

Downloads628

Electrical Equipment Identification Based on YOLOv5s and Android Deployment

[HTML] PDF下载 (628)

[1]LIAO Xiaohui,XIE Zichen,LU Mingshuo.Electrical Equipment Identification Based on YOLOv5s and Android Deployment[J].Journal of Zhengzhou University (Engineering Science),2024,45(01):122-128.[doi:10.13705/j.issn.1671-6833.2024.01.004]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 01 Page number: 122-128 Column: Public date: 2024-01-19

Title:: Electrical Equipment Identification Based on YOLOv5s and Android Deployment

Author(s):: LIAO Xiaohui; XIE Zichen; LU Mingshuo; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: electrical equipment; improved of YOLOv5s; Android; TensorFlow Lite; image identification

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2024.01.004

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 percentage points higher than that of the original model. After deployment, the APP had the interface of equipment recognition and equipment introduction, and the recognition time of each image was less than 1 s when using mobile terminal, 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.

References:: [1] 钱金戈, 徐丹, 史豪杰. 图像识别技术在变电站屏柜运检中的应用[J].工业控制计算机, 2021, 34(3)： 7-8, 11.QIAN J G, XU D, SHI H J. Application of operation and maintenance of substation cabinet based on image recognition[J].Industrial Control Computer, 2021, 34(3)：7-8, 11.
[2] LOWE D G. Object recognition from local scale-invariant features[C]∥Proceedings of the Seventh IEEE International Conference on Computer Vision. Piscataway： IEEE, 2002： 1150-1157.
[3] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR′05). Piscataway： IEEE, 2005： 886-893.
[4] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7)： 971-987.
[5] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE, 2014： 580-587.
[6] GIRSHICK R. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV). Piscataway： IEEE, 2015： 1440-1448.
[7] 李文璞, 谢可, 廖逍, 等. 基于Faster RCNN变电设备红外图像缺陷识别方法[J].南方电网技术, 2019, 13(12)： 79-84.LI W P, XIE K, LIAO X, et al. Intelligent diagnosis method of infrared image for transformer equipment based on improved Faster RCNN[J].Southern Power System Technology, 2019, 13(12)：79-84.
[8] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once： unified, real-time object detection[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway： IEEE, 2016： 779-788.
[9] 吕禾丰, 陆华才. 基于YOLOv5算法的交通标志识别技术研究[J].电子测量与仪器学报, 2021, 35(10)： 137-144.LYU H F, LU H C. Research on traffic sign recognition technology based on YOLOv5 algorithm[J].Journal of Electronic Measurement and Instrumentation, 2021, 35(10)： 137-144.
[10] 张晓鹏, 许志远, 曲胜, 等. 基于改进YOLOv5深度学习的海上船舶识别算法[J].大连海洋大学学报, 2022, 37(5)：866-872.ZHANG X P, XU Z Y, QU S, et al. Recognition algorithm of marine ship based on improved YOLOv5 deep learning[J].Journal of Dalian Ocean University, 2022, 37(5)：866-872.
[11] 周逸博, 马毓涛, 赵艳茹. 基于YOLOv5s和Android的苹果树皮病害识别系统设计[J].广东农业科学, 2022, 49(10)：155-163.ZHOU Y B, MA Y T, ZHAO Y R. Design of mobile APP recognition system for apple bark disease based on YOLOv5s and Android[J].Guangdong Agricultural Sciences, 2022, 49(10)：155-163.
[12] 院老虎, 常玉坤, 刘家夫. 基于改进YOLOv5s的雾天场景车辆检测方法[J].郑州大学学报(工学版), 2023, 44(3)： 35-41.YUAN L H, CHANG Y K, LIU J F. Vehicle detection method based on improved YOLOv5s in foggy scene[J].Journal of Zhengzhou University (Engineering Science), 2023, 44(3)： 35-41.
[13] 唐靓, 余明慧, 武明虎, 等. 基于改进YOLOv5的绝缘子缺陷检测算法[J].华中师范大学学报(自然科学版), 2022, 56(5)： 771-780.TANG J, YU M H, WU M H, et al. Insulator defect detection algorithm based on improved YOLOv5[J].Journal of Central China Normal University (Natural Sciences), 2022, 56(5)： 771-780.
[14] 曾水玲, 唐敏之. 基于模糊推理的电气设备红外图像分割[J].红外技术, 2023, 45(5)： 446-454.ZENG S L, TANG M Z. Infrared image segmentation for electrical equipment based on fuzzy inference[J].Infrared Technology, 2023, 45(5)： 446-454.
[15] 任杰, 高岭, 于佳龙, 等. 面向边缘设备的高能效深度学习任务调度策略[J].计算机学报, 2020, 43(3)： 440-452.REN J, GAO L, YU J L, et al. Energy-efficient deep learning task scheduling strategy for edge device[J].Chinese Journal of Computers, 2020, 43(3)：440-452.
[16] OR AN I L, SEICULESCU C, C LEANU C D. Benchmarking TensorFlow Lite quantization algorithms for deep neural networks[C]∥2022 IEEE 16th International Symposium on Applied Computational Intelligence and Informatics (SACI). Piscataway： IEEE, 2022： 221-226.
[17] SHIN D J, KIM J J. A deep learning framework perfor-mance evaluation to use YOLO in Nvidia Jetson platform[J].Applied Sciences, 2022, 12(8)： 3734.
[18] 李双峰. TensorFlow Lite：端侧机器学习框架[J].计算机研究与发展, 2020, 57(9)： 1839-1853.LI S F. TensorFlow Lite： on-device machine learning framework[J].Journal of Computer Research and Deve-lopment, 2020, 57(9)： 1839-1853.

Similar References:

Memo

Last Update: 2024-01-25