Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset

Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient soluti...

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Bibliographic Details
Main Authors: Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Smart Cities
Subjects:
SAR operations
UAVs
deep learning
DINO
occlusion detection
Disaster Occlusion Detection Dataset (DODD)
Online Access:https://www.mdpi.com/2624-6511/8/1/12
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