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Updated: Sep 13, 2025

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Video Instance Segmentation Through Hierarchical Offset Compensation and Temporal Memory Update for UAV Aerial

Ying Huang1, Yinhui Zhang1, Zifen He1

  • 1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China.

Sensors (Basel, Switzerland)
|July 30, 2025
PubMed
Summary

This study introduces a novel method for video instance segmentation (VIS) using unmanned aerial vehicles (UAVs), improving accuracy for deforming targets by enhancing feature capture and temporal modeling.

Keywords:
intelligent inspectionunmanned aerial vehicle (UAV) aerial imagesvideo instance segmentation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Existing video instance segmentation (VIS) methods fail to accurately segment deforming targets in unmanned aerial vehicle (UAV) footage.
  • Challenges include ineffective feature offset capture and inadequate temporal correlation modeling, leading to inconsistent results.

Purpose of the Study:

  • To propose a hierarchical offset compensation and temporal memory update method for video instance segmentation (HT-VIS) with high generalization ability.
  • To improve the accuracy and robustness of VIS for irregularly deforming targets in UAV applications.

Main Methods:

  • Developed a hierarchical offset compensation (HOC) module for deformable offset across frames, capturing spatial motion features sequentially and in parallel.
  • Implemented a temporal memory update (TMU) module using convolutional long-short-term memory (ConvLSTM) to model temporal dynamic context and update frame features.

Main Results:

  • The proposed HT-VIS method demonstrated superior performance on the YouTubeVIS-2019 and a self-built UAV-Seg datasets.
  • Achieved state-of-the-art results, outperforming CrossVIS by up to 3.9% and SipMask by 2.1% on specific datasets.

Conclusions:

  • The HT-VIS framework effectively addresses limitations in segmenting deforming targets for UAV intelligent inspection tasks.
  • The method shows significant improvements in average segmentation accuracy and demonstrates robustness across diverse datasets.