Video Instance Segmentation Through Hierarchical Offset Compensation and Temporal Memory Update for UAV Aerial Images
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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