UCL: Unsupervised Curriculum Learning for Utility Pole Detection from Aerial Imagery-Reference-Cited by-同舟云学术

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UCL: Unsupervised Curriculum Learning for Utility Pole Detection from Aerial Imagery

Published:2022-11-30 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
language:
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Author:

Abid Nosheen¹,Kovács György¹,Wedin Jacob¹,Paszkowsky Nuria Agues²,Shafait Faisal³,Liwicki Marcus¹

Affiliation:

1. Luleå Tekniska Universitet,EISLAB Machine Learning,Sweden

2. Research Institutes of Sweden,Sweden

3. National University of Sciences and Technology,Deep Learning Lab, National Center of Artificial Intelligence,Pakistan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10034558/10034559/10034610.pdf?arnumber=10034610

Reference32 articles.

1. A combined analysis to extract objects in remote sensing images

2. Measuring the distance of vegetation from powerlines using stereo vision

3. Monitoring of Overhead Transmission Lines: A Review from the Perspective of Contactless Technologies

4. Corner detection and matching for visual tracking during power line inspection

5. VR-PROUD: Vehicle Re-identification using PROgressive Unsupervised Deep architecture

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding of leaning utility poles for visual monitoring of power distribution infrastructure;Journal of Civil Structural Health Monitoring;2024-06-21

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