Abstract: UAV infrared target detection technology represents an important research direction combing infrared imaging, computer vision, and artificial intelligence. Characterized by such advantages—all-weather and round-the-clock sensing capabilities, insensitivity to lighting conditions, and high adaptability to complex environments—it demonstrates broad application prospects in fields such as public safety, low-altitude airspace governance, border patrol, and emergency rescue.In recent years, deep learning, particularly single-stage object detection frameworks represented by the YOLO series, has shown a superior speed-accuracy trade-off in UAV infrared target detection. Addressing typical challenges such as weak infrared imaging signals, small target scales, severe background interference, and limited onboard computing power, related researches have continuously made new breakthroughs in feature enhancement, multi-scale fusion, attention mechanism design, lightweight network architectures, and detection head optimization. These advancements have effectively propelled the technology toward higher accuracy, stronger robustness, and better deploy ability.To comprehensively present the latest research findings and development trends in this field, Issue 12, 2025 of Infrared Technology has launched a special issue on "Infrared Detection on UAV Platforms," featuring several research papers. The included works systematically review the research progress of UAV infrared target detection based on the YOLO framework and provide an in-depth exploration of the detection mechanisms and improvement pathways for infrared small targets in complex backgrounds. Furthermore, focusing on critical issues such as the collaborative modeling of local and global information, multi-scale feature fusion, receptive field and spatial attention mechanisms, lightweight network design, and efficient bounding box regression strategies, these studies propose a series of targeted model architectures and algorithmic solutions. Their effectiveness and potential for engineering applications have been validated on typical infrared UAV datasets.Finally, we would like to express our sincere gratitude to all the authors of the papers in this special column and to the reviewers for their invaluable contributions.