摘要
为了实现安全高效的覆冰检测,基于计算机视觉的机器学习模型层出不穷。然而,随着电网规模的不断扩大和发展,传统的覆冰检测模型面临数据分布差异和数据量剧增的挑战,这严重影响了检测效率。为此,本文引入了持续学习策略,提出了一种层次敏感的视觉持续学习提示方法。该方法通过为每一层编码器学习软提示,有效克服了数据分布差异和应对了数据量剧增的问题,从而提高了模型的鲁棒性和泛化能力。为了验证所提出模型的有效性,我们将其与现有的最先进方法进行了比较,并在不同持续学习设置下进行了测试,包括类增量、域增量和任务无关设置。实验结果表明,LP-VCL在平均准确率和遗忘率两个关键指标上均优于基线方法,特别是在类增量和域增量设置下表现突出,证明了该方法的优越性和实用价值。
In order to realize safe and efficient ice-cover detection,machine learning models based on computer vision have emerged.However,with the continuous expansion and development of the power grid scale,the traditional ice-cover detection models face the challenges of the difference in data distribution and the dramatic increase in data volume,which seriously affects the detection efficiency.To this end,we introduce a continuous learning strategy and proposes a layer-aware prompting for visual continual learning(LP-VCL).The method effectively overcomes the problem of data distribution differences and copes with the dramatic increase in data volume by learning soft cues for each layer of encoder,thus improving the robustness and generalization ability of the model.To validate the effectiveness of the proposed model,we compare it with existing state-of-the-art methods and test it under different continuous learning settings,including class increment,domain increment,and task-independent settings.The experimental results show that LP-VCL outperforms the baseline method in two key metrics,namely average accuracy and forgetting rate,especially in the class-increment and domain-increment settings,which proves the superiority and practical value of the method.
作者
任孝东
杨旗
钟尧
杨柳青
张露松
毛先胤
REN Xiaodong;YANG Qi;ZHONG Yao;YANG Liuqing;ZHANG Lusong;MAO Xianyin(Electrical Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,Guizhou,China;Joint Laboratory of Ice Prevention&Disaster Reducing of China Southern Power Grid Co.Ltd.,Guiyang 550002,Guizhou,China)
出处
《电力大数据》
2024年第3期33-41,共9页
Power Systems and Big Data
基金
南方电网有限责任公司科技项目(GZKJXM20220059)。
关键词
覆冰检测
持续学习
编码器
ice cover detection
continuous learning
encoders
