论文标题
部分可观测时空混沌系统的无模型预测
YouTube-GDD: A challenging gun detection dataset with rich contextual information
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
An automatic gun detection system can detect potential gun-related violence at an early stage that is of paramount importance for citizens security. In the whole system, object detection algorithm is the key to perceive the environment so that the system can detect dangerous objects such as pistols and rifles. However, mainstream deep learning-based object detection algorithms depend heavily on large-scale high-quality annotated samples, and the existing gun datasets are characterized by low resolution, little contextual information and little data volume. To promote the development of security, this work presents a new challenging dataset called YouTube Gun Detection Dataset (YouTube-GDD). Our dataset is collected from 343 high-definition YouTube videos and contains 5000 well-chosen images, in which 16064 instances of gun and 9046 instances of person are annotated. Compared to other datasets, YouTube-GDD is "dynamic", containing rich contextual information and recording shape changes of the gun during shooting. To build a baseline for gun detection, we evaluate YOLOv5 on YouTube-GDD and analyze the influence of additional related annotated information on gun detection. YouTube-GDD and subsequent updates will be released at https://github.com/UCAS-GYX/YouTube-GDD.
