SpectraSentinel: LightWeight Dual-Stream Real-Time Drone Detection, Tracking and Payload Identification
Department of Computer Science and Engineering（计算机科学与工程系）
Shahriar Kabir, Istiak Ahmmed Rifti, H. M. Shadman Tabib, Mushfiqur Rahman, Sadatul Islam Sadi, Hasnaen Adil, Ahmed Mahir Sultan Rumi, Ch Md Rakin Haider
无人机在民用空域的普及引发了紧迫的安全担忧，亟需稳健的实时监视系统。针对2025年VIP Cup挑战赛的无人机检测、跟踪与载荷识别任务，我们提出了一种双流无人机监控框架。我们的方法在并行的红外（热成像）和可见光（RGB）数据流上部署独立的You Only Look Once v11-nano（YOLOv11n）目标检测器，刻意避免早期融合。这种分离设计让每个模型都能针对其输入模态的独特特性进行专门优化，以应对不同环境条件下小型空中目标带来的独特挑战。我们针对每个领域定制了数据预处理和增强策略——例如限制红外图像的颜色抖动——并微调训练超参数，以提升在强噪声、低光照和运动模糊条件下的检测性能。最终得到的轻量级YOLOv11n模型在区分无人机与鸟类、分类载荷类型方面展现出高精度，同时保持了实时性能。本报告详细阐述了双模态设计的原理、专门的训练流程，以及共同实现了RGB和红外通道下高效准确无人机监视的架构优化。
英文摘要：
The proliferation of drones in civilian airspace has raised urgent security concerns, necessitating robust real-time surveillance systems. In response to the 2025 VIP Cup challenge tasks - drone detection, tracking, and payload identification - we propose a dual-stream drone monitoring framework. Our approach deploys independent You Only Look Once v11-nano (YOLOv11n) object detectors on parallel infrared (thermal) and visible (RGB) data streams, deliberately avoiding early fusion. This separation allows each model to be specifically optimized for the distinct characteristics of its input modality, addressing the unique challenges posed by small aerial objects in diverse environmental conditions. We customize data preprocessing and augmentation strategies per domain - such as limiting color jitter for IR imagery - and fine-tune training hyperparameters to enhance detection performance under conditions of heavy noise, low light, and motion blur. The resulting lightweight YOLOv11n models demonstrate high accuracy in distinguishing drones from birds and in classifying payload types, all while maintaining real-time performance. This report details the rationale for a dual-modality design, the specialized training pipelines, and the architectural optimizations that collectively enable efficient and accurate drone surveillance across RGB and IR channels.
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos
State Key Laboratory of Optical Instrumentation, Zhejiang University, China（浙江大学光学仪器国家重点实验室）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany（卡尔斯鲁厄理工学院人机与机器人研究所）
Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理空间研究 institute FGI，芬兰国家土地测绘局）
Centerness-based Instance-aware Knowledge Distillation with Task-wise Mutual Lifting for Object Detection on Drone Imagery
Beihang University（北京航空航天大学）；State Key Laboratory of Software Development Environment（软件开发环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
