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:2019,28(5):248-251
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基于Mask R-CNN的电力设备锈迹检测
(中国石油大学(华东) 计算机与通信工程学院, 青岛 266580)
Rust Detection of Power Equipment Based on Mask R-CNN
(College of Computer & Communication Engineering, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2018-12-03    修订日期:2018-12-25
中文摘要: 电力设备锈迹目标的识别在电力安全方面具有极高的应用价值,但是锈迹具有大小、形状不规则等特点,利用传统的机器学习算法检测效率和准确率不高.针对这一问题,研究分析锈迹特点,提出基于Mask R-CNN的电力设备锈迹检测识别方法.使用Faster R-CNN完成目标检测的功能,FCN精准的完成语义分割的功能,实现像素级别的分类识别,较好地解决了不规则锈迹的检测问题.实验结果表明,基于Mask R-CNN的电力设备锈迹检测结果准确率高.
Abstract:The recognition of rust target on power equipment has very high application value in power security, nevertheless, the rust has the characteristics of irregular size and shape, thus the detection efficiency and accuracy of traditional machine learning algorithm are not high. Aiming at this problem, the characteristics of rust stain are studied and analyzed, and a rust detection and recognition method for power equipment based on Mask R-CNN is proposed. Faster R-CNN is used to complete the function of target detection, FCN accurately completes the function of semantics segmentation, realizes the classification and recognition at the pixel level, and better solves the problem of irregular rust detection. The experimental results show that the accuracy of rust detection of power equipment based on Mask R-CNN is high.
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基金项目:国家自然科学基金(61309024);山东省重点研发计划(2017GGX0140)
引用文本:
薛冰.基于Mask R-CNN的电力设备锈迹检测.计算机系统应用,2019,28(5):248-251
XUE Bing.Rust Detection of Power Equipment Based on Mask R-CNN.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):248-251

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