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Related Experiment Video

Updated: Sep 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach.

Miloš S Pavlović^1,2, Petar D Milanović^1,2, Miloš S Stanković^2,3

¹School of Electrical Engineering, University of Belgrade, Bul. Kralja Aleksandara 73, 11120 Belgrade, Serbia.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for creating SWIR datasets using visible light, significantly boosting deep learning object detection for surveillance. This advances multi-spectral imaging systems (MSIS) for critical applications.

Keywords:

SWIR imaging cross-spectral data annotation deep learning multi-sensor imaging system object detection

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Area of Science:

Computer Vision
Remote Sensing
Image Processing

Background:

Short-Wave Infrared (SWIR) imaging offers advantages in challenging conditions, crucial for multi-spectral imaging systems (MSIS) in surveillance.
Deep learning (DL) object detection enhances MSIS for long-range monitoring, but SWIR datasets for DL training are scarce.
Current limitations in SWIR data hinder the performance of DL models in complex surveillance scenarios.

Purpose of the Study:

To develop an automatic, cross-spectral data annotation methodology for generating SWIR channel training datasets.
To improve object detection performance in SWIR images captured in challenging outdoor environments.
To address the lack of sufficient training data for DL-based object detection in the SWIR spectrum.

Main Methods:

Proposed a cross-spectral automatic data annotation methodology using visible-light channels to generate SWIR training data.
Developed a mathematical image transformation to align visible-light detected objects with SWIR images, accounting for distortions.
Utilized a state-of-the-art YOLOX model for experimental validation on object detection tasks.

Main Results:

Retraining the YOLOX model with the automatically generated SWIR dataset significantly improved average detection precision.
Demonstrated substantial performance gains in object detection for cars and persons across various YOLOX model variants (nano, tiny, x).
Validated the effectiveness of the cross-spectral methodology in enhancing SWIR image analysis for surveillance.

Conclusions:

The proposed cross-spectral data annotation method effectively overcomes the limitations of SWIR dataset scarcity for DL.
This approach significantly enhances object detection capabilities in SWIR imaging for challenging outdoor surveillance.
The methodology provides a scalable solution for improving DL model performance in multi-spectral imaging systems.