  Vehicle detection in real-time scenarios is challenging because of the time
constraints and the presence of multiple types of vehicles with different
speeds, shapes, structures, etc. This paper presents a new method relied on
generating a confidence map-for robust and faster vehicle detection. To reduce
the adverse effect of different speeds, shapes, structures, and the presence of
several vehicles in a single image, we introduce the concept of augmentation
which highlights the region of interest containing the vehicles. The augmented
map is generated by exploring the combination of multiresolution analysis and
maximally stable extremal regions (MR-MSER). The output of MR-MSER is supplied
to fast CNN to generate a confidence map, which results in candidate regions.
Furthermore, unlike existing models that implement complicated models for
vehicle detection, we explore the combination of a rough set and fuzzy-based
models for robust vehicle detection. To show the effectiveness of the proposed
method, we conduct experiments on our dataset captured by drones and on several
vehicle detection benchmark datasets, namely, KITTI and UA-DETRAC. The results
on our dataset and the benchmark datasets show that the proposed method
outperforms the existing methods in terms of time efficiency and achieves a
good detection rate.
