Drone-Based Tower Survey by Multi-Task Learning

Mirza Tanzim Sami, Da Yan, Huang Huang, Xinyu Liang, Guimu Guo, Zhe Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Various industries use towers as part of their daily operations, such as transmission towers (aka. electricity pylons), telecommunications towers and water towers. These towers re- quire regular maintenance, and before the maintenance work can be done, a preliminary survey must be conducted to determine where to work. More and more, such surveys are being conducted via drones. This work develops a detection model to help locate tower issues from the video frames of drones. However, it does not provide satisfactory performance to directly train such an object detection model with the annotated problem locations from domain experts. Therefore, we propose to improve the quality of the extracted image features with the help of another separate task which detects the various parts that are involved in the tower issues, such as bolts, nuts, washers and pins, the annotations of which can be done without the need of domain expertise. Through this multi-task learning scheme, we improved the problem detection recall from 59.6% to 71.5%, providing much more effective recommendations of potential issues for inspectors to examine further. Also, the average number of problem detections in each image is merely 5.54 so inspectors are not overwhelmed by the recommended locations.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages3
ISBN (Electronic)9781665439022
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021


Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems


Dive into the research topics of 'Drone-Based Tower Survey by Multi-Task Learning'. Together they form a unique fingerprint.

Cite this