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.

翻译：实时场景下的车辆检测因时间约束以及多种类型车辆（具有不同速度、形状、结构等）的存在而具有挑战性。本文提出一种基于生成置信度图的新方法，以实现鲁棒且更快的车辆检测。为降低单张图像中不同速度、形状、结构及多车辆存在的不利影响，我们引入了增强概念，突出包含车辆的感兴趣区域。通过探索多分辨率分析与最大稳定极值区域（MR-MSER）的组合生成增强图。MR-MSER的输出被输入快速CNN以生成置信度图，从而得到候选区域。此外，与现有采用复杂模型进行车辆检测的方法不同，我们探索了粗糙集与模糊模型的组合以实现鲁棒车辆检测。为验证所提方法的有效性，我们在无人机采集的数据集以及多个车辆检测基准数据集（即KITTI和UA-DETRAC）上进行了实验。我们的数据集与基准数据集上的结果表明，所提方法在时间效率上优于现有方法，并取得了良好的检测率。