[1]刘召,张黎明,耿美晓,等.基于改进的Faster R-CNN高压线缆目标检测方法[J].智能系统学报,2019,14(4):627-634.[doi:10.11992/tis.201905026]
LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14(4):627-634.[doi:10.11992/tis.201905026]
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LIU Zhao,ZHANG Liming,GENG Meixiao,et al.Object detection of high-voltage cable based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2019,14(4):627-634.[doi:10.11992/tis.201905026]
基于改进的Faster R-CNN高压线缆目标检测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第4期
页码:
627-634
栏目:
学术论文—机器学习
出版日期:
2019-07-02
- Title:
- Object detection of high-voltage cable based on improved Faster R-CNN
- Keywords:
- object detection; deep learning; high-voltage cable; complicated background; small object; live working; Faster R-CNN; region proposal
- 分类号:
- TP18;TP391
- 摘要:
- 利用带电作业机器人取代人类的手动作业，可以有效地减少高电压、强电场对人体的危害，大大提高作业的效率。为解决带电作业机器人在复杂背景环境中对线缆目标的智能检测问题，提出基于改进的Faster R-CNN高压线缆目标检测方法。为了提高网络提取图像高级特征的能力，引入跳转连接并调整激活层、卷积层的顺序；然后对候选框生成机制进行改进，提升网络对小目标检测的性能；最后利用ROI池化层提取每个区域的特征，同时完成分类和框回归任务。通过构建高压线缆图像数据集，基于改进的Faster R-CNN模型进行大量实验，最后取得了较好的精度和较快的速度。
- Abstract:
- The use of live working robots to replace human manual operation can effectively reduce the harm of a high-voltage and strong electric field to the human body and considerably improve the working efficiency. To solve the intelligent high-voltage cable object detection problem for live working robots under a complicated background environment, a high-voltage cable object detection method based on the improved Faster R-CNN is proposed. To improve the capability of extracting the high-level features of images in the network, skip connections are introduced and the order of the activation and convolution layers is adjusted. Then, the proposal bounding box generation mechanism is improved to enhance the performance of the proposed method for small object detection. Finally, the features of each region are extracted using the ROI pooling layers, and the classification and bounding box regression tasks are accomplished at the same time. Through the construction of high-voltage cable image datasets and the performance of numerous experiments based on the improved Faster R-CNN model, good accuracy and fast speed have been achieved.
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备注/Memo
收稿日期:2019-05-14。
基金项目:天津市智能制造科技重大专项（17ZXZNGX00120）.
作者简介:刘召,男,1979年生,博士,主要研究方向为机器人及其自动化装备。作为项目负责人和技术骨干完成研究课题10余项,其中,863项目1项,985项目1项,国防科技课题4项,日本学术振兴会科学研究补助金基础研究项目1项。发表学术论文10余篇;张黎明,男,1969年生,高级技师,主要研究方向为智能配电网。个人获24项国家专利,带领团队为公司实现技术革新400多项,获国家专利158项,20余项填补智电网建设空白;耿美晓,女,1990年生,硕士,主要研究方向为图像处理、机器人学习与人工智能。
通讯作者:耿美晓.E-mail:lin@thtcrobot.com
更新日期/Last Update:
2019-08-25