AIDNet: Detecting Insulators and Defects from Satellite Remote Sensing Images: An Exploration

The insulator has a diameter of about 200 mm and occupies only 0.4 pixels in the best satellite remote sensing image (SRSI). Detecting insulators and self-explosion defects from SRSI is a challenging task due to the extremely low spatial resolution and mixed pixels issues. Algorithm design that impr...

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Bibliographic Details
Published in2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC) pp. 347 - 353
Main Authors Zhou, Fangrong, Ma, Yi, Wen, Gang, Ma, Yutang, Lan, Zhicai, Zhang, Zhengde
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2022
Subjects
Adaptive systems
Computers
deep learning
defects
detection
Filtering
Insulators
mixed pixels
neural networks
Object detection
satellite remote sensing images
Satellites
Task analysis
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Summary:The insulator has a diameter of about 200 mm and occupies only 0.4 pixels in the best satellite remote sensing image (SRSI). Detecting insulators and self-explosion defects from SRSI is a challenging task due to the extremely low spatial resolution and mixed pixels issues. Algorithm design that improves the required spatial resolution (rSR) is one way to advance this task. This paper proposes a two-stage detection method combining an adaptive network and a detection network, using multiple filtering and adaptive enhancement methods to achieve end-to-end detection, which can effectively improve the rSR. The idea proposed in this paper can promote the realization of goals that detect insulators and defects from SRSI. The proposed algorithm can realize the detection at relatively poor spatial resolution, and can be applied to small targets detection in general object detection tasks.
DOI:10.1109/ICNISC57059.2022.00075