基于改进Faster RCNN的小尺度入侵目标识别及定位

doi:10.11930/j.issn.1004-9649.202006190

中国电力 ›› 2021, Vol. 54 ›› Issue (3): 38-44.DOI: 10.11930/j.issn.1004-9649.202006190

• 国家“十三五”智能电网重大专项专栏：（六）先进计算与人工智能技术专栏 • 上一篇下一篇

基于改进Faster RCNN的小尺度入侵目标识别及定位

马静怡¹, 崔昊杨¹, 张明达², 孙益辉², 许永鹏³

1. 上海电力大学电子与信息工程学院，上海 200090;
2. 国网浙江省电力公司奉化供电有限公司，浙江宁波 315500;
3. 上海交通大学电气工程系，上海 200240

收稿日期:2020-06-17 修回日期:2021-10-02 出版日期:2021-03-05 发布日期:2021-03-17
作者简介:马静怡(1995-)，女，硕士研究生，从事图像处理与目标检测研究，E-mail：296546698@qq.com;崔昊杨(1978-)，男，通信作者，博士，教授，从事电力设备状态检测、红外探测与检测研究，E-mail：cuihy@shiep.edu.cn
基金资助:
国家自然科学基金资助项目(61872230，61107081)

Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN

MA Jingyi¹, CUI Haoyang¹, ZHANG Mingda², SUN Yihui², XU Yongpeng³

1. Department of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. Fenghua Power Supply Company, State Grid Zhejiang Electric Power Company, Ningbo 315500, China;
3. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2020-06-17 Revised:2021-10-02 Online:2021-03-05 Published:2021-03-17
Supported by:
This work is supported by the National Natural Science Foundation of China (No.61872230, No.61107081)

摘要/Abstract

摘要： 为实现无人值守变电站视频监控系统对动态小尺寸入侵目标体的识别与定位，提出一种基于改进Faster RCNN的快速神经网络辨识方法。该方法通过构建深度卷积网络计算目标样本的强语义特征，并利用密集连接的传输通道融合位置信息，从而得到适应于小目标检测的基础骨干网络；然后利用锚框挑选出目标可能存在的区域，采用双线性插值法计算定位框的坐标以实现像素级别的精确定位。使用采集的变电站监控图像对模型进行训练，得到适应小尺寸异物的改进Faster RCNN检测模型。通过对比实验结果表明，所提改进方法在进行小尺寸异物检测时能够保持高精度并具有时效性，具备一定的工程实用价值。

关键词: 小目标检测, 深度学习, 卷积神经网络, Faster RCNN, 双线性插值

Abstract: In order to realize the recognition and location of dynamic small-scale intrusion targets with the video monitoring system in unattended substations, a fast neural network identification method based on improved Faster RCNN is proposed. In this method, the strong semantic features of the target samples are calculated by constructing the deep convolution network, and the location information is fused using the densely connected transmission channels, so as to obtain the basic backbone network suitable for small target detection. Then, the candidate region of the target is generated with the region proposal network, and the coordinates of the location frame are calculated using the bilinear interpolation method to achieve the accurate positioning at the pixel level. The model is trained based on the actual image sample set, and the improved Faster RCNN detection model is obtained. The experimental results show that the improved method can maintain high accuracy and timeliness in detection of small-scale foreign objects, and has a certain value for engineering application.

Key words: small-scale object detection, deep learning, convolution neural network, Faster RCNN, bilinear interpolation

马静怡, 崔昊杨, 张明达, 孙益辉, 许永鹏. 基于改进Faster RCNN的小尺度入侵目标识别及定位[J]. 中国电力, 2021, 54(3): 38-44.

MA Jingyi, CUI Haoyang, ZHANG Mingda, SUN Yihui, XU Yongpeng. Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN[J]. Electric Power, 2021, 54(3): 38-44.

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https://www.electricpower.com.cn/CN/Y2021/V54/I3/38

参考文献

[1] 张巧霞, 王广民, 李江林, 等. 变电站远程运维平台设计与实现[J]. 电力系统保护与控制, 2019, 47(10): 164-172.
ZHANG Qiaoxia, WANG Guangmin, LI Jianglin, et al. Design and implementation of substation remote operation and maintenance platform[J]. Power System Protection and Control, 2019, 47(10): 164-172.
[2] 周俊煌, 黄廷城, 谢小瑜, 等. 视频图像智能识别技术在输变电系统中的应用研究综述[J/OL]. 中国电力: 1-12[2020-08-07]. http://kns.cnki.net/kcms/detail/11.3265.TM.20200414.1351.004.html.
ZHOU Junhuang, HUANG Tingcheng, XIE Xiaoyu, et al. Review of application research of video image intelligent recognition technology in power transmission and distribution systems[J]. Electric Power: 1-12[2020-08-07]. http://kns.cnki.net/kcms/detail/11.3265.TM.20200414.1351.004.html.
[3] 章立. 可见光图像弱小目标的检测与跟踪研究[D]. 西安: 西安科技大学, 2018.
ZHANG Li. Research on detection and tracking of weak small target in visible light image[D]. Xi'an: Xi'an University of Science and Technology, 2018.
[4] 赵振兵, 王乐. 一种航拍绝缘子串图像自动定位方法[J]. 仪器仪表学报, 2014, 35(3): 558-565
ZHAO Zhenbing, WANG Le. Aerial insulator string image automatic location method[J]. Chinese Journal of Scientific Instrument, 2014, 35(3): 558-565
[5] 冯玲, 黄新波, 朱永灿. 基于图像处理的输电线路覆冰厚度测量[J]. 电力自动化设备, 2011, 31(10): 76-80
FENG Ling, HUANG Xinbo, ZHU Yongcan. Transmission line icing thickness measuring based on image processing[J]. Electric Power Automation Equipment, 2011, 31(10): 76-80
[6] KULKARNI A, CALLAN J. Selective search[J]. ACM Transactions on Information Systems, 2015, 33(4): 1-33.
[7] XIANG P, ZHOU H X, LI H, et al. Hyperspectral anomaly detection by local joint subspace process and support vector machine[J]. International Journal of Remote Sensing, 2020, 41(10): 3798-3819.
[8] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE, 2015: 1440-1448.
[9] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[10] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA. IEEE, 2016: 779-788.
[11] 王万国, 田兵, 刘越, 等. 基于RCNN的无人机巡检图像电力小部件识别研究[J]. 地球信息科学学报, 2017, 19(2): 256-263
WANG Wanguo, TIAN Bing, LIU Yue, et al. Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J]. Journal of Geo-Information Science, 2017, 19(2): 256-263
[12] 林刚, 王波, 彭辉, 等. 基于改进Faster-RCNN的输电线巡检图像多目标检测及定位[J]. 电力自动化设备, 2019, 39(5): 213-218
LIN Gang, WANG Bo, PENG Hui, et al. Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN[J]. Electric Power Automation Equipment, 2019, 39(5): 213-218
[13] 李文璞, 谢可, 廖逍, 等. 基于Faster RCNN变电设备红外图像缺陷识别方法[J]. 南方电网技术, 2019, 13(12): 79-84
LI Wenpu, XIE Ke, LIAO Xiao, et al. Intelligent diagnosis method of infrared image for transformer equipment based on improved faster RCNN[J]. Southern Power System Technology, 2019, 13(12): 79-84
[14] 韩松臣, 张比浩, 李炜, 等. 基于改进Faster-RCNN的机场场面小目标物体检测算法[J]. 南京航空航天大学学报, 2019, 51(6): 735-741
HAN Songchen, ZHANG Bihao, LI Wei, et al. Small target detection in airport scene via modified faster-RCNN[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2019, 51(6): 735-741
[15] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA. IEEE, 2016: 770-778.
[16] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA. IEEE, 2017: 936-944.
[17] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA. IEEE, 2017: 2261-2269.
[18] NEUBECK A, VAN GOOL L. Efficient non-maximum suppression[C]//18th International Conference on Pattern Recognition (ICPR'06). Hong Kong, China. IEEE, 2006: 850-855.
[19] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2015: 3431-3440.
[20] KRIZHEVSKY A, SUTSKEVER I, HINTON G. Imagenet classification with deep convolutional neural networks[C]// Conference and Workshop on Neural Information Processing Systems. 2012: 1097-1105.
[21] 刘建伟, 赵会丹, 罗雄麟, 等. 深度学习批归一化及其相关算法研究进展[J]. 自动化学报, 2020, 46(6): 1090-1120
LIU Jianwei, ZHAO Huidan, LUO Xionglin, et al. Research progress on batch normalization of deep learning and its related algorithms[J]. Acta Automatica Sinica, 2020, 46(6): 1090-1120
[22] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[J]. Journal of Machine Learning Research, 2011, 15: 315-323.
[23] 谢美华, 王正明. 基于图像梯度信息的插值方法[J]. 中国图象图形学报, 2005, 10(7): 856-861
XIE Meihua, WANG Zhengming. Image interpolation based on gradient[J]. Journal of Image and Graphics, 2005, 10(7): 856-861
[24] DING H J, PAN Z P, CEN Q, et al. Multi-scale fully convolutional network for gland segmentation using three-class classification[J]. Neurocomputing, 2020, 380(7): 150-161.
[25] 龚钢军, 张帅, 吴秋新, 等. 基于TensorFlow的高压输电线路异物识别[J]. 电力自动化设备, 2019, 39(4): 204-209, 216
GONG Gangjun, ZHANG Shuai, WU Qiuxin, et al. Foreign body identification based on TensorFlow for high voltage transmission line[J]. Electric Power Automation Equipment, 2019, 39(4): 204-209, 216
[26] 万吉林, 王慧芳, 管敏渊, 等. 基于Faster R-CNN和U-Net的变电站指针式仪表读数自动识别方法[J]. 电网技术, 2020, 44(8): 3097-3105
WAN Jilin, WANG Huifang, GUAN Minyuan, et al. An automatic identification for reading of substation pointer-type meters using faster R-CNN and U-Net[J]. Power System Technology, 2020, 44(8): 3097-3105

基于改进Faster RCNN的小尺度入侵目标识别及定位

Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN

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基于改进Faster RCNN的小尺度入侵目标识别及定位

Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN

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