改进的Fast-CNN模型在绝缘子特征检测中的研究  

Research on Infrared Insulator Detection Based on Improved Fast-CNN Mode

作　　者：纪超 黄新波[1] 曹雯[1] 朱永灿[1] 张烨[1] JI Chao;HUANG Xin-bo;CAO Wen;ZHU Yong-can;ZHANG Ye(School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《计算机与现代化》2019年第4期59-64,71,共7页Computer and Modernization

基　　金：国家自然科学基金资助项目(51707141);陕西省自然科学基础研究计划项目(2017JQ6054);西安工程大学博士启动基金资助项目(BS1505);陕西省重点科技创新团队计划项目(2014KCT-16);陕西省科学技术研究发展计划项目(2014XT-07);陕西省工业科技攻关项目(2015GY-075)

摘　　要：针对目前电网巡检系统中采用红外成像检测绝缘子串特征的效果受环境影响,提出联合显著区域和Fast-CNN网络(改进后的卷积神经网络)用于绝缘子特征检测研究。显著区域检测首先采用超像素描述各区域位置的整体信息;然后基于各超像素的特征协方差信息计算各超像素的显著度得到大致显著区域;再通过区域模块化和局部复杂度对比提取显著特征,同时将2种方法提取的显著特征分别输入改进后的Fast-CNN网络进行显著区域检测,同时引入动态自适应池化模型和余弦窗处理中间层,最后通过多次迭代训练得到绝缘子特征,避免CNN模型耗时的全图搜索。将本文算法在红外图像库中进行测试,本文算法的F-Measure以及平均误差MAE均优于当前流行算法。The detection effect of infrared image insulator strings is affected by the environment in the power grid inspection. The combination of saliency detection and improved convolution neural network (Fast-CNN network) is proposed for insulator feature detection. Firstly, superpixels are used to describe the overall information of each region, saliency features are calculated based on the characteristic covariance information of each superpixel. Then the salient features are extracted by regional modular extraction and local complexity contrast. At the same time, the salient features extracted from the two methods are respectively input into the improved Fast-CNN network for salient region detection, a dynamic adaptive pool model is proposed, the cosine window is introduced to deal with the middle layer. Finally, the characteristics of insulators are obtained through iterative training. It can avoid full graph search for the CNN model. The proposed algorithm is tested in the infrared image library, the F-Measure and the average error MAE of the proposed algorithm are better than the current popular algorithms.

关 键 词：机器视觉 深度学习 显著性计算 绝缘子检测 快速卷积神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]