Application of Target Detection Algorithm in Infrared Image

Dear Colleagues,

With the growing demand for intelligent surveillance, autonomous systems, and remote sensing applications, infrared (IR) imaging has emerged as a critical modality for robust visual perception under challenging conditions such as low light, fog, or total darkness. In particular, target detection in infrared images presents unique opportunities and challenges, driven by the physics of thermal radiation and the limitations of current sensing technologies.

Recent years have witnessed significant progress that has dramatically improved detection performance. However, infrared imagery still poses persistent difficulties due to low spatial resolution, low contrast, and domain gaps with visible-spectrum data. As such, there is a growing need to develop specialized techniques tailored to infrared signals.

This Special Issue aims to highlight the latest research and developments in infrared target detection, with a focus on algorithm design, data-driven methods, real-time deployment, and cross-domain adaptation. We welcome both research articles and comprehensive reviews addressing theoretical advancements and practical implementations.

Topics of interest include, but are not limited to, the following:

• Infrared object detection algorithms
• Deep learning for thermal imagery
• Small-target detection and tracking in infrared
• Domain adaptation between visible and infrared modalities
• Sensor fusion and multi-modal detection
• Infrared dataset creation and augmentation techniques
• Real-time and edge deployment of IR detection systems
• Applications in defense, surveillance, automotive, and environmental monitoring

Dr. Feras Almasri
Dr. Olivier Debeir
Dr. Bruno Da Silva
Guest Editors

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