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Machine Learning-Based Human Detection Using Active Non-Line-of-Sight Laser Sensing.

Semra Çelebi¹, İbrahim Türkoğlu²

¹Faculty of Engineering, Department of Computer Engineering, Siirt University, Siirt 56000, Türkiye.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This study demonstrates reliable non-line-of-sight (NLOS) human detection using single-photon avalanche diode (SPAD) sensors. Random Forest machine learning models effectively identify hidden individuals in simulated disaster scenarios.

Keywords:

NLOS deep learning laser signals non-line of sight remote sensing

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Area of Science:

Photonics and Sensing
Machine Learning for Security
Search and Rescue Technologies

Background:

Active non-line-of-sight (NLOS) human detection utilizes indirect photon reflections to identify hidden individuals.
Simulated post-disaster rubble environments were used to test sensing capabilities.

Purpose of the Study:

To evaluate the effectiveness of a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC) system for NLOS human detection.
To compare the performance of machine learning models in classifying human presence based on time-photon waveforms.

Main Methods:

A SPAD and TCSPC system acquired time-photon waveforms in controlled NLOS environments.
Data preprocessing involved normalization, histogram shaping, and signal windowing.
Convolutional Neural Network, Gated Recurrent Unit, and Random Forest models were trained for classification.

Main Results:

All models achieved 100% sensitivity in detecting human presence.
Random Forest exhibited the highest overall accuracy and specificity, particularly in human-absent scenarios.
Tree-based classifiers demonstrated superior robustness to statistical variations under limited photon conditions.

Conclusions:

Low-cost SPAD-based NLOS sensing systems offer reliable human detection in indirect-observation scenarios.
Random Forest models are effective for classifying human presence using time-photon histogram data.
The study highlights the potential of photon-based sensing for search and rescue operations.