论文标题
不断学习即时检测人：无人机的生物风格的视觉系统
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
论文作者
论文摘要
本文首次证明了配备有峰值依赖性可塑性（STDP）的生物学上可见的尖峰神经网络（SNN）可以不断学会使用基于视网膜的，基于事件的相机来检测飞行者。我们的管道工作如下。首先，将一系列事件数据（$ <2 $分钟）通过飞行无人机捕获人类的人类，转发到卷积的SNNSTDP系统，该系统还从读数（形成半监督系统）接收教师尖峰信号。然后，停止了STDP适应，并在测试序列上评估了学习的系统。我们进行了几项实验，以研究系统中关键参数的影响，并将其与常规训练的CNN进行比较。我们表明，与具有基于事件的相机框架的CNN相比，我们的系统达到了较高的峰值$ F_1 $得分（+19％），同时可以在线适应。
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn to detect walking people on the fly using retina-inspired, event-based cameras. Our pipeline works as follows. First, a short sequence of event data ($<2$ minutes), capturing a walking human by a flying drone, is forwarded to a convolutional SNNSTDP system which also receives teacher spiking signals from a readout (forming a semi-supervised system). Then, STDP adaptation is stopped and the learned system is assessed on testing sequences. We conduct several experiments to study the effect of key parameters in our system and to compare it against conventionally-trained CNNs. We show that our system reaches a higher peak $F_1$ score (+19%) compared to CNNs with event-based camera frames, while enabling on-line adaptation.
