  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.
