College of Computing and Information Sciences, University of Technology and Applied Sciences-AL Mussanah, Muladdah P.O. Box 191, Oman.
Sensors (Basel). 2024 Aug 23;24(17):5455. doi: 10.3390/s24175455.
Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.
鸟击是一个严重的航空安全问题，可能会对飞机部件造成严重损害，甚至导致乘客死亡。针对这种日益增加的趋势，迫切需要实施新的、更有效的检测和预防技术。本文提出了一种新的深度学习模型，旨在提高机场条件下的飞机安全性，以检测和缓解鸟击问题。该研究基于一个包含不同物种和飞行模式的鸟类图像的广泛数据库，采用复杂的图像增强技术，生成多种飞机操作场景，确保模型在不同条件下具有鲁棒性。该方法围绕构建时空卷积神经网络展开，该网络采用空间注意力结构和动态时间处理，精确识别飞行鸟类。这项研究的一个重要特点是其双焦点模型的架构，该模型由两个组件组成，即基于注意力的时间分析网络和具有空间意识的卷积神经网络。该模型的架构可以识别嵌套在拥挤和移动背景中的特定特征，从而降低误报率并提高检测精度。该模型的注意力机制通过识别鸟类飞行模式的关键特征来增强模型的注意力。结果表明，与现有的鸟类检测系统相比，所提出的模型在准确性和实时响应方面表现更好。消融研究表明了每个组件的不可或缺的作用，证实了它们对提高检测性能的协同作用。该研究证实了该模型作为机场鸟击监测系统的一部分的适用性，为预防策略提供了一种替代方案。这项工作受益于深度学习特征应用的独特性，为处理鸟击问题提供了一种大规模、可靠的工具。
