论文标题
基于视频的烟熏车辆通过粗到精细的框架检测
Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework
论文作者
论文摘要
视频中的自动烟熏车辆检测是用于环境保护机构的紫外线遥控遥控器的较高解决方案。但是，将车辆烟雾与后车辆或杂物道路的阴影和湿区区分开来是一个挑战，并且由于注释数据有限，可能会更糟。在本文中，我们首先引入了一个现实世界中的大型烟熏车数据集，其中有75,000个带注释的烟熏车辆图像，从而有助于对先进的深度学习模型进行有效培训。为了启用公平算法比较，我们还构建了一个烟熏车辆视频数据集，其中包括163个带有细分级注释的长视频。此外，我们提出了一个新的粗到烟熏车辆检测（代码）框架，以进行有效的烟熏车辆检测。这些代码首先利用轻质Yolo检测器以高召回率进行快速烟雾检测，然后采用烟极匹配策略来消除非车辆烟雾，最后使用精心设计的3D模型进一步完善空间时间空间的结果。四个指标的广泛实验表明，我们的框架比基于手工的特征方法和最近的高级方法要优越。代码和数据集将在https://github.com/pengxj/smokyvehicle上发布。
Automatic smoky vehicle detection in videos is a superior solution to the traditional expensive remote sensing one with ultraviolet-infrared light devices for environmental protection agencies. However, it is challenging to distinguish vehicle smoke from shadow and wet regions coming from rear vehicle or clutter roads, and could be worse due to limited annotated data. In this paper, we first introduce a real-world large-scale smoky vehicle dataset with 75,000 annotated smoky vehicle images, facilitating the effective training of advanced deep learning models. To enable fair algorithm comparison, we also build a smoky vehicle video dataset including 163 long videos with segment-level annotations. Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection. The CoDeS first leverages a light-weight YOLO detector for fast smoke detection with high recall rate, and then applies a smoke-vehicle matching strategy to eliminate non-vehicle smoke, and finally uses a elaborately-designed 3D model to further refine the results in spatial temporal space. Extensive experiments in four metrics demonstrate that our framework is significantly superior to those hand-crafted feature based methods and recent advanced methods. The code and dataset will be released at https://github.com/pengxj/smokyvehicle.
