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Storage tank detection in remote sensing images based on circular bounding boxes and large selective kernel.

Yu Liu1, Yong Wan2, Weimin Huang3

  • 1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China.

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|December 18, 2025
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Summary

This study introduces a novel method using circular bounding boxes and a Large Selective Kernel (LSK) for accurate storage tank detection in remote sensing images, improving methane emission monitoring from the oil and gas industry.

Keywords:
Circular bounding boxMethaneRemote sensingStorage tanksTarget detection.

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Area of Science:

  • Environmental monitoring
  • Remote sensing technology
  • Greenhouse gas detection

Background:

  • Methane emissions from oil and gas are significant.
  • Existing storage tank detection methods have limitations.
  • Deep learning models struggle with small, multi-scale objects and background interference.

Purpose of the Study:

  • To develop an improved method for storage tank detection in remote sensing images.
  • To enhance the accuracy of methane emission source identification.
  • To support environmental sustainability in the oil and gas sector.

Main Methods:

  • Integration of circular bounding boxes for stable Intersection over Union (IoU).
  • Implementation of a Large Selective Kernel (LSK) for dynamic receptive field adjustment.
  • Utilizing a YOLO-v10 framework with a comprehensive dataset.

Main Results:

  • Achieved precision of 0.911, recall of 0.902, and mAP@0.5 of 0.931.
  • Demonstrated improvements of 2.0% in precision, 2.7% in recall, and 1.8% in mAP@0.5 over the YOLO-v10 baseline.
  • Successfully addressed challenges with small objects, multi-scale features, and background interference.

Conclusions:

  • The novel method offers a robust solution for storage tank detection.
  • Improved accuracy aids in pinpointing methane emission sources.
  • The approach contributes to environmental monitoring and sustainability efforts.