  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.
