ArticleScientific data2025
An infrared dataset for partially occluded person detection in complex environment for search and rescue.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Field-Validated UAV-Based Deep Learning Framework for Automated Inspection of Power Transmission and Distribution Infrastructure.Sensors (Basel, Switzerland) · 2026Article
- High-resolution thermal infrared dataset for airborne person detection in SAR missions.Scientific data · 2026Article
- An aerial color image anomaly dataset for search missions in complex forested terrain.Scientific data · 2026Article
- An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation.Scientific data · 2025Article
- Advancing Remote Life Sensing for Search and Rescue: A Novel Framework for Precise Vital Signs Detection via Airborne UWB Radar.Sensors (Basel, Switzerland) · 2025Article
- YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System.Biomimetics (Basel, Switzerland) · 2025Article
- An infrared dataset for partially occluded person detection in complex environment for search and rescue.Scientific data · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
The combination of unmanned aerial vehicles (UAVs) and deep learning has potential applicability in various complex search and rescue scenes. However, due to the presence of environmental occlusions such as trees, the performance of UAVs mounted with different optical payloads in detecting missing persons is poor. To the best of our knowledge, currently available non-occluded human target datasets are insufficient to address the challenges of automatic recognition for partially occluded human targets. To address this problem, we collected a UAV-based infrared thermal imaging dataset for outdoor, partially occluded person detection (POP). POP is composed of 8768 labeled thermal images collected from different environmental scenes. After training with popular object detection networks, our dataset performed stable average precision for partially occluded person detection and short response time. In addition, high precision of object detection by POP trained networks was not attenuated until the occlusion rate exceeded 70%. We expected POP would extend present methodologies for the search of human objects under complex occluded circumstances.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.