摘要
针对高压杆塔上存在的鸟巢给输电线路带来的安全隐患,提出一种基于改进的LBP特征训练AdaBoost分类器的高压杆塔鸟巢检测算法.经过测试表明,该方法有助于提高人机交互能力,可以极大减轻电力工作人员的工作负担,具有一定的应用价值.
For the potential dangers of transmission lines caused by bird nests on high-voltage towers,a high-voltage tower bird nest detection algorithm based on the improved LBP feature training AdaBoost classifier is proposed.The results of experiments show that this method has application value in that it can help to improve the human-computer interaction ability,and greatly reduce the workload of power workers.
作者
师飘
Shi Piao(Department of Electronics and Information Engineering,Bozhou University,Bozhou 236800,China)
出处
《洛阳师范学院学报》
2020年第8期40-45,共6页
Journal of Luoyang Normal University
基金
安徽省高校优秀青年人才支持计划项目(gxyq2017109)
安徽省质量工程项目(2019sxzx24)
亳州学院质量工程教学团队项目(2018jxtd02)
亳州学院创客实验室项目(2017cksy02)
亳州学院项目(BYZ2019C05)
亳州学院校级科研重点项目(BYZ2019B01)。
