Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN
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 samp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2021-03-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006190 |
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| _version_ | 1850053427798736896 |
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| author | Jingyi MA Haoyang CUI Mingda ZHANG Yihui SUN Yongpeng XU |
| author_facet | Jingyi MA Haoyang CUI Mingda ZHANG Yihui SUN Yongpeng XU |
| author_sort | Jingyi MA |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c0e797939c0344149a2c1e3676661eef |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2021-03-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-c0e797939c0344149a2c1e3676661eef2025-08-20T02:52:31ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492021-03-01543384410.11930/j.issn.1004-9649.202006190zgdl-54-02-majingyiSmall Scale Invade-Target Recognition and Location Based on Improved Faster RCNNJingyi MA0Haoyang CUI1Mingda ZHANG2Yihui SUN3Yongpeng XU4Department of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaDepartment of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaFenghua Power Supply Company, State Grid Zhejiang Electric Power Company, Ningbo 315500, ChinaFenghua Power Supply Company, State Grid Zhejiang Electric Power Company, Ningbo 315500, ChinaDepartment of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaIn 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006190small-scale object detectiondeep learningconvolution neural networkfaster rcnnbilinear interpolation |
| spellingShingle | Jingyi MA Haoyang CUI Mingda ZHANG Yihui SUN Yongpeng XU Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN Zhongguo dianli small-scale object detection deep learning convolution neural network faster rcnn bilinear interpolation |
| title | Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN |
| title_full | Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN |
| title_fullStr | Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN |
| title_full_unstemmed | Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN |
| title_short | Small Scale Invade-Target Recognition and Location Based on Improved Faster RCNN |
| title_sort | small scale invade target recognition and location based on improved faster rcnn |
| topic | small-scale object detection deep learning convolution neural network faster rcnn bilinear interpolation |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006190 |
| work_keys_str_mv | AT jingyima smallscaleinvadetargetrecognitionandlocationbasedonimprovedfasterrcnn AT haoyangcui smallscaleinvadetargetrecognitionandlocationbasedonimprovedfasterrcnn AT mingdazhang smallscaleinvadetargetrecognitionandlocationbasedonimprovedfasterrcnn AT yihuisun smallscaleinvadetargetrecognitionandlocationbasedonimprovedfasterrcnn AT yongpengxu smallscaleinvadetargetrecognitionandlocationbasedonimprovedfasterrcnn |