With the increasing utilization of Internet of Things (IoT) enabled drones in diverse applications like photography, delivery, and surveillance, concerns regarding privacy and security have become more prominent. Drones have the ability to capture sensitive information, compromise privacy, and pose security risks. As a result, the demand for advanced technology to automate drone detection has become crucial. This paper presents a project on a transfer-based drone detection scheme, which forms an integral part of a computer vision-based module and leverages transfer learning to enhance performance. By harnessing the knowledge of pre-trained models from a related domain, transfer learning enables improved results even with limited training data. To evaluate the scheme's performance, we conducted tests on benchmark datasets, including the Drone-vs-Bird Dataset and the UAVDT dataset. Notably, the scheme's effectiveness is highlighted by its IOU-based validation results, demonstrating the potential of deep learning-based technology in automating drone detection in critical areas such as airports, military bases, and other high-security zones.

翻译：随着物联网（IoT）无人机在摄影、配送和监控等多样化应用中的日益普及，隐私和安全问题愈发凸显。无人机具备捕获敏感信息、侵犯隐私并带来安全风险的能力。因此，对自动化无人机检测先进技术的需求变得至关重要。本文提出了一项基于迁移的无人机检测方案，该方案作为计算机视觉模块的组成部分，通过迁移学习提升性能。通过利用相关领域预训练模型的知识，迁移学习即使在训练数据有限的情况下也能实现更优结果。为评估方案性能，我们在基准数据集（包括Drone-vs-Bird数据集和UAVDT数据集）上进行了测试。值得注意的是，基于IoU的验证结果凸显了该方案的有效性，展示了深度学习技术在机场、军事基地及其他高安全等级区域实现自动化无人机检测的潜力。