Human Remains Detection Natural Disasters is a M.Tech project topic for Computer Science & Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Human Remains Detection Natural Disasters Project Details
| Abstract |
Post‑disaster search and rescue teams often work on dangerous terrain with limited gear. Finding dead victims in hard‑to‑reach spots is essential for quick recovery and safety. This study uses a deep‑learning computer‑vision method built on the You Only Look Once (YOLO) object‑detection framework in Python. The goal is to automatically spot human remains in images taken after a disaster. The model was trained on special datasets that mimic the messy conditions of real disasters, so it can help rescue crews and volunteers. Tests show the system can identify human bodies with 96 % accuracy even in tough situations. Still, real‑world use faces problems like different levels of occlusion, harsh weather,
and distortions from aerial views, which need more testing. For M.Tech researchers, the work offers a solid base for measuring performance, speeding up inference, and comparing different YOLO versions for emergency‑response tasks.
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| Reference Paper |
Human Remains Detection in Natural Disasters using YOLO: A Deep Learning Approach |
| Domain |
Computer Science & Engineering |
| Sub-Domain |
Artificial Intelligence & Machine Learning / Natural Language Processing |
| PDF Download |
Download / View PDF |
| Get Help |
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