Bidirection-Fusion-YOLOv3: An Improved Method for Insulator Defect Detection Using UAV Image
Author:
Affiliation:
1. School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, China
Funder
Natural Science Basic Research Program of Shaanxi-Shanmei Joint Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09866802.pdf?arnumber=9866802
Reference28 articles.
1. YOLO9000: Better, Faster, Stronger
2. YOLOv3: An incremental improvement;redmon;arXiv 1804 02767,2018
3. Detection and Recognition for Fault Insulator Based on Deep Learning
4. Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks
5. Study on Power Line Insulator Defect Detection via Improved Faster Region-Based Convolutional Neural Network
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