基于R-FCN区域全卷积网络的绝缘子红外图像识别研究
Techniques of Automation and Applications（2023）
国网河南省电力公司
Abstract
红外热成像因其具有非接触性、灵敏性等优点,已被广泛应用于电力设备的带电检测及其诊断中.其中,对设备快速精确地识别定位是电力设备智能诊断的关键.然而利用传统机器算法对电力设备图像进行识别定位,存在泛化能力不强、鲁棒性较差等不足.针对此问题,开展基于R-FCN区域全卷积网络的绝缘子红外图像识别研究.在TensorFlow框架下搭建R-FCN检测模型,并利用迁移学习方法初始化模型权重,以提高训练效果.最后,将所研究算法与Faster-RCNN和SSD模型进行对比.实验表明,R-FCN模型的检测精度为89.2%,检测速度为23 fps,具有较高的精度和速度.该算法为绝缘子的智能诊断奠定坚实基础.
MoreKey words
insulator,regional fully convolutional network,R-FCN model,infrared image
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined