输电通道图像监拍训练模型数据

该数据以架空输电线路隐患智能识别解决方案为载体，结合客户的需求制定数据搜集以及标注规范，对线上数据进行回收、筛选、分类、清洗并标注之后，可用于深度学习模型训练开发，经过训练的模型最终以云端服务或者边缘端算法方案集成到解决方案内，可替代传统的人工肉眼巡视，大大减少了人工的干预，提高告警准确性，避免跳闸等事故的发生，减少经济损失；可减少运维人员工作量，减轻一线巡视人员的工作负担，保障巡视人员的人身安全，降低输电线路运检的人力成本，节约运维成本；电网运行更安全，提高电网服务质量和水平，产生良好社会效益。

This dataset is developed based on the intelligent hidden danger identification solution for overhead transmission lines, with data collection and annotation specifications formulated in accordance with customer needs. After collecting, screening, classifying, cleaning and annotating the online data, it can be used for the training and development of deep learning models. The trained models can be integrated into the solution in the form of cloud services or edge-side algorithm schemes, replacing traditional manual visual inspection. This can greatly reduce manual intervention, improve the accuracy of alarm notifications, avoid accidents such as line trips and reduce economic losses. Additionally, it can cut down the workload of operation and maintenance personnel, alleviate the work burden of front-line inspection staff, ensure the personal safety of inspectors, reduce the labor costs for overhead transmission line operation and inspection, and save operation and maintenance expenditures. Moreover, it enables safer power grid operation, improves the quality and level of power grid services, and generates good social benefits.

搜集汇总

数据集介绍

特点

该数据集主要用于架空输电线路隐患智能识别，通过深度学习模型训练开发，可提高告警准确性、减少人工干预和运维成本。数据集应用于架空输电线路业务场景通道隐患识别，训练得到的算法模型可用于隐患识别告警。