Unmanned aerial vehicles (UAVs) are commonly used for edge collaborative computing in current transmission line object detection, where computationally intensive tasks generated by user nodes are offloaded to more powerful edge servers for processing. However, performing edge collaborative processing on transmission line image data may result in serious privacy breaches. To address this issue, we propose a secure single-stage detection model called SecYOLOv7 that preserves the privacy of object detecting. Based on secure multi-party computation (MPC), a series of secure computing protocols are designed for the collaborative execution of Secure Feature Contraction, Secure Bounding-Box Prediction and Secure Object Classification by two non-edge servers. Performance evaluation shows that both computational and communication overhead in this framework as well as calculation error significantly outperform existing works.

翻译：无人机广泛应用于当前输电线路目标检测中的边缘协作计算，用户节点产生的计算密集型任务被卸载至性能更强的边缘服务器进行处理。然而，对输电线路图像数据进行边缘协作处理可能导致严重的隐私泄露。针对这一问题，我们提出了一种名为SecYOLOv7的隐私保护单阶段检测模型。基于安全多方计算（MPC），我们设计了一系列安全计算协议，通过两个非边缘服务器协同执行安全特征压缩、安全边界框预测和安全目标分类。性能评估表明，该框架在计算开销、通信开销以及计算误差方面均显著优于现有方法。