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Related Experiment Video

Updated: Aug 23, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Multispectral Benchmark Dataset and Baseline for Forklift Collision Avoidance.

Hyeongjun Kim¹, Taejoo Kim¹, Won Jo¹

¹Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea.

Sensors (Basel, Switzerland)

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces a new multispectral pedestrian detection method for automated forklifts, enhancing safety by identifying workers and estimating distances. The developed dataset and SSD 2.5D detector improve accident prevention in intralogistics.

Keywords:

2.5D detection automated forklifts collision avoidance intralogistics multispectral pedestrian detection

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Area of Science:

Computer Vision
Robotics
Industrial Safety

Background:

Automated forklifts require enhanced safety features to prevent collisions with workers.
Existing pedestrian detection methods lack applicability in intralogistics due to domain-specific challenges.
Multispectral data (RGB and thermal) offers potential for robust pedestrian detection.

Purpose of the Study:

To develop a multispectral pedestrian detection system for automated forklifts.
To create a novel dataset for multispectral pedestrian detection in intralogistics.
To propose a detector capable of both detecting pedestrians and estimating their distance.

Main Methods:

Collected and curated a unique multispectral (RGB and thermal) pedestrian dataset for intralogistics.
Developed a novel image alignment technique using sensor disparity and camera geometry, avoiding beam splitters.
Proposed the SSD 2.5D multispectral pedestrian detector for detection and distance estimation.

Main Results:

The SSD 2.5D detector demonstrated effective pedestrian detection and centroid localization.
Performance was validated using driving-domain metrics adapted for intralogistics zones (hazardous, warning).
The proposed method shows promise for improving safety in automated intralogistics environments.

Conclusions:

The developed dataset and SSD 2.5D detector significantly advance multispectral pedestrian detection in intralogistics.
The system enhances human-aware properties for automated forklifts, contributing to accident prevention.
This research provides a foundation for safer human-robot collaboration in industrial settings.