Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos
The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3...
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World Scientific Publishing
2024-01-01
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370123500058 |
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| author | Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei |
| author_facet | Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei |
| author_sort | Guanghong Deng |
| collection | DOAJ |
| description | The application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3[Formula: see text] image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3[Formula: see text] algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1[Formula: see text]ms and 13.2[Formula: see text]ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7[Formula: see text]ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7[Formula: see text]ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly improved the accuracy and efficiency of intelligent inspection of substation meter readings. |
| format | Article |
| id | doaj-art-0d82a7ce9bdd48dfab6a16c8c05a77f4 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-0d82a7ce9bdd48dfab6a16c8c05a77f42025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370123500058Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection PhotosGuanghong Deng0Liangpei Lin1Baihao Lin2Wenlong Jing3Xiaodan Zhao4Yong Li5Rui Yang6Shidun Xie7Pengfeng Wei8GuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaThe application of the combination of unmanned aerial vehicles (UAVs) and artificial intelligence is a hot topic in the intelligent inspection of substations, and meter reading is a very challenging task. This paper proposes a method based on the combination of YOLOv6n object detection and Deeplabv3[Formula: see text] image segmentation and performs post-processing on the segmented images to obtain meter readings. First, YOLOv6n is used to detect the meter area of the aerial image and classify the meters. Second, the detected meter images are fed into the image segmentation model. The backbone network of the Deeplabv3[Formula: see text] algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. Third, License Plate Recognition Network (LPRNet) is used to recognize digital meter images. In order to solve the problem of inaccurate pointer meter readings, to begin with, the segmented image is corroded; in addition, the circular dial area is flattened into a rectangular area by concentric circle sampling method. Finally, the meter reading is calculated by the position of the pointer, the scale and the total range of the meter. The post-processing part uses numba to optimize the inference speed. The experimental results show that in two datasets, The mean average precision of 50 (mAP50) accuracy of the YOLOv6n model using this method reached 99.71% and 98.60%, respectively, and the inference speed of a single image was 17.1[Formula: see text]ms and 13.2[Formula: see text]ms, respectively. The mean intersection over union (mIoU) of the image segmentation model reached 82.00%, 74.73%, 73.50%, 82.26% and 73.20%, respectively, and the single segmentation speed reached 33.7[Formula: see text]ms. The LPRNet model has a recognition accuracy of 99.17% and a single image inference speed of 14.7[Formula: see text]ms. At the same time, several mainstream object detection and semantic segmentation algorithms are compared. The experimental results show that the method in this paper greatly improved the accuracy and efficiency of intelligent inspection of substation meter readings.https://www.worldscientific.com/doi/10.1142/S2972370123500058Object detectionimage segmentationYOLOv6nDeeplabv3meter reading |
| spellingShingle | Guanghong Deng Liangpei Lin Baihao Lin Wenlong Jing Xiaodan Zhao Yong Li Rui Yang Shidun Xie Pengfeng Wei Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos Computing Open Object detection image segmentation YOLOv6n Deeplabv3 meter reading |
| title | Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos |
| title_full | Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos |
| title_fullStr | Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos |
| title_full_unstemmed | Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos |
| title_short | Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos |
| title_sort | design of command and dispatch system for automatic reading of meter images in substations by uav inspection photos |
| topic | Object detection image segmentation YOLOv6n Deeplabv3 meter reading |
| url | https://www.worldscientific.com/doi/10.1142/S2972370123500058 |
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