A New YOLOv4-tiny Neural Network and Its Application on Object Detection of Power-line Isolators
Conforming to the rapid increasing requirements of fast and intelligent inspection of power lines, the idea of installing edge device on aircraft for intelligent inspection is put forward. The Resblock-D lightweight network is selected as the feature extraction network, and the new YOLOv4-tiny algor...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2022-12-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2160 |
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| Summary: | Conforming to the rapid increasing requirements of fast and intelligent inspection of power lines, the idea of installing edge device on aircraft for intelligent inspection is put forward. The Resblock-D lightweight network is selected as the feature extraction network, and the new YOLOv4-tiny algorithm and deep network based on the standard YOLOv4-tiny structure is designed as the Resblock-D and the CSPDarknet53-tiny are used as the main backbone.Then, the related GPU version Darknet deep network frame was built on Jetson NANO for training, deploying and testing.On basis of the power line images sets from ImageNet standard library and the street shot power line pictures, the isolator data set is established accordingly in the format of standard Pascal VOC.Under the Darknet deep network frame, the detector train command is utilized to train the new YOLOv4-tiny network without the pre-training weight file successfully. Selecting the weight file of the highest mAP(mean average precision), the isolator intelligent identification experiment was carried out on Jetson NANO. In term of 58% weight files, 66.7% computation, higher 10% detection quantity and lower 10% false detection quantity, it is proved that the new YOLOv4-tiny algorithm is efficient and practical in comparison to the standard YOLOv4-tiny algorithm. |
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| ISSN: | 1007-2683 |