Robust Aerial Person Detection With Lightweight Distillation Network for Edge Deployment

Aerial person detection (APD) is vital for enhancing search and rescue (SaR) operations, particularly when locating victims in remote, poorly-lit areas. Despite advancements in detection technologies, achieving a balance between detection speed and accuracy on mobile devices in "edge AI" continues to pose challenges. In this article, a lightweight distillation network (APDNet) is proposed for edge deployment of APD, which enables real-time inference as well as minimizes accuracy loss during model transfer. The proposed APDNet employs a distillation network between varying-depth backbones and integrates an 8-bit quantized optimizer to reduce the floating-point operations of network parameters. Specifically, in the teach-assistant distillation (TAD) stage, small student models using random weight initialization are trained with pseudo-labels generated by deeper teacher models, facilitating consistent learning for a more accurate, lighter model. Moreover, a low-precision quantization (LPQ) stage incorporates an offline, quantization-aware training strategy that dynamically adjusts the ranges of weight and activation function float-point values, reducing computational complexity. In order to compensate for the potential accuracy decline, a pluggable tracker updates the position and feature information of persons frame-by-frame, with tracking results integrated with detection outputs to enhance accuracy. Extensive experiments on the Heridal, Manipal-UAV, and VTSaR datasets confirm the effectiveness of APDNet, demonstrating its superior performance in edge-based APD.