红外技术2025，Vol.47Issue(10)：1246-1254,9.
NFZ-YOLOv10:面向机场管制区的红外目标检测算法
NFZ-YOLOv10:Infrared Target Detection Algorithm for Airport Control Zones
摘要
Abstract
To address the high computational complexity and poor detection performance of small targets in infrared scenarios,an improved target-detection algorithm,NFZ-YOLOv10,is proposed.By introducing the lightweight network StarNet,the computational load and parameter count of the model were reduced,and the feature extraction capability was enhanced.The neck network was optimized,and the slim-neck paradigm was constructed using the GSConv and VOVGSCSP modules,achieving a deep integration of high-level semantic information and fine-grained spatial features.The NWD loss function was introduced to effectively alleviate the sensitivity of traditional loss functions to positional deviations in small-scale target-detection tasks,thereby significantly improving the detection accuracy of the model.The experimental results showed that the mean average precision of NFZ-YOLOv10 on the self-built dataset reached 94.5％,an increase of 2.7％compared to YOLOv10n.Additionally,the computational cost and the number of parameters of NFZ-YOLOv10 have been reduced by 24.4％and 22.3％,respectively,compared to YOLOv10n.The single-frame image detection time is as low as 4.5 ms,and the model size has been shrunk to 4.9 MB.In summary,NFZ-YOLOv10 achieves a more effective balance between detection accuracy and complexity and has strong application potential in the detection of low-slow-small targets within airport control zones.关键词
民航安全/红外图像/YOLOv10/轻量化/弱小目标检测Key words
civil aviation safety/infrared image/YOLOv10/lightweight/dim target detection分类
信息技术与安全科学引用本文复制引用
陈俊吉,陆安江,彭熙舜,王梦莹,赵文培,冯曦,任登红..NFZ-YOLOv10:面向机场管制区的红外目标检测算法[J].红外技术,2025,47(10):1246-1254,9.基金项目
贵州省自然科学基金项目(黔科合基础-ZK[2023]一般055) （黔科合基础-ZK[2023]一般055）
贵州省科技支撑计划项目(黔科合支撑[2023]一般465). （黔科合支撑[2023]一般465）
