基于深度学习的多级分类用于航空安全
Deep Learning Based Multi-Level Classification for Aviation Safety
AI总结：
本文提出基于深度学习的多级分类方法，用于识别鸟类种类、估计鸟群类型和大小，以提升航空安全的预测能力。
摘要：
鸟击对航空安全构成重大威胁，通常导致人员伤亡、严重飞机损坏和重大经济损失。现有的鸟击预防策略主要依赖于鸟类雷达系统，用于实时检测和跟踪鸟类。这些系统的重大局限性在于无法识别鸟的种类，这是一个关键因素，因为不同物种表现出不同的飞行行为和海拔偏好。为了解决这一挑战，我们提出了一种基于图像的鸟类分类框架，使用卷积神经网络（CNNs），以与摄像头系统配合进行自主视觉检测。CNN被设计用来识别鸟类种类，并提供关键输入到针对特定物种的预测模型中，以实现准确的飞行路径预测。除了物种识别外，我们还实现了专用的CNN分类器来估计鸟群形成类型和鸟群大小。这些特征为航空安全提供了有价值的信息补充。具体而言，鸟群类型和大小提供了关于集体飞行行为和轨迹分散的见解。鸟群大小直接与潜在影响的严重性相关，因为多个鸟类的总体损坏风险随着多个鸟类的动能总和而增加。
英文摘要：
Bird strikes pose a significant threat to aviation safety, often resulting in loss of life, severe aircraft damage, and substantial financial costs. Existing bird strike prevention strategies primarily rely on avian radar systems that detect and track birds in real time. A major limitation of these systems is their inability to identify bird species, an essential factor, as different species exhibit distinct flight behaviors, and altitudinal preference. To address this challenge, we propose an image-based bird classification framework using Convolutional Neural Networks (CNNs), designed to work with camera systems for autonomous visual detection. The CNN is designed to identify bird species and provide critical input to species-specific predictive models for accurate flight path prediction. In addition to species identification, we implemented dedicated CNN classifiers to estimate flock formation type and flock size. These characteristics provide valuable supplementary information for aviation safety. Specifically, flock type and size offer insights into collective flight behavior, and trajectory dispersion . Flock size directly relates to the potential impact severity, as the overall damage risk increases with the combined kinetic energy of multiple birds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
