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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features-Gaussian Mixture Model

Meng Lv¹, Haoting Liu¹, Mengmeng Wang¹

¹Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces an all-weather surveillance algorithm using mid-infrared images for grassland monitoring. The novel approach enhances safety during grazing bans by accurately detecting small targets in complex environments.

Keywords:

Gaussian Mixture Model (GMM)UNet grassland monitoring image segmentation mid-infrared image

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Area of Science:

Environmental Science
Computer Vision
Artificial Intelligence

Background:

Intelligent video monitoring is crucial for natural resource protection.
All-weather surveillance is needed for effective grassland management, especially during grazing prohibition periods.

Purpose of the Study:

To propose a multi-object segmentation algorithm for all-weather grassland surveillance.
To improve the accuracy and efficiency of target detection in complex outdoor environments.

Main Methods:

Integration of Image Timing Features-Gaussian Mixture Model (ITF-GMM) for robust background modeling.
Development of a Convolutional-UNet (Con-UNet) model replacing max-pooling with convolutional layers for enhanced feature extraction.
Implementation of an integrated computation strategy combining ITF-GMM and Con-UNet outputs with morphological operations for refined segmentation.

Main Results:

Achieved high performance metrics: 96.92% precision, 99.87% accuracy, 94.81% IOU, and 97.75% recall.
Demonstrated real-time processing capabilities.
Successfully enhanced small-target detection in challenging outdoor conditions.

Conclusions:

The proposed algorithm effectively enhances grassland monitoring and enforcement automation.
The integration of ITF-GMM and Con-UNet provides a robust solution for all-weather surveillance.
The method proves capable of improving safety monitoring during critical grassland management periods.