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.

中文翻译：

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具有用于边缘部署的轻量级蒸馏网络的强大空中人员检测


空中人员检测 (APD) 对于加强搜救 (SaR) 行动至关重要，特别是在偏远、光线不足的地区定位受害者时。尽管检测技术取得了进步，但在“边缘人工智能”移动设备上实现检测速度和准确性之间的平衡仍然面临挑战。在本文中，提出了一种用于 APD 边缘部署的轻量级蒸馏网络（APDNet），它可以实现实时推理，并最大限度地减少模型传输过程中的精度损失。所提出的 APDNet 在不同深度主干之间采用蒸馏网络，并集成 8 位量化优化器以减少网络参数的浮点运算。具体来说，在助教蒸馏（TAD）阶段，使用随机权重初始化的小型学生模型使用更深层次的教师模型生成的伪标签进行训练，从而促进一致的学习，从而获得更准确、更轻量的模型。此外，低精度量化 (LPQ) 阶段采用离线量化感知训练策略，可动态调整权重和激活函数浮点值的范围，从而降低计算复杂性。为了补偿潜在的准确性下降，可插式跟踪器逐帧更新人员的位置和特征信息，并将跟踪结果与检测输出集成以提高准确性。在 Heridal、Manipal-UAV 和 VTSaR 数据集上进行的大量实验证实了 APDNet 的有效性，展示了其在基于边缘的 APD 中的卓越性能。