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首页> 外文会议>NAFOSTED Conference on Information and Computer Science >Vehicle Detection at Night Time
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Vehicle Detection at Night Time

机译：夜间的车辆检测

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Recent growth in deep learning has opened up many opportunities for the problem of vehicle detection. Detecting objects in poor visibility is catching scientists attention. In this study, we choose night as the challenge. We conducted training and evaluation of the YOLOv4 method in combination with image preprocessing methods: gamma, CycleGAN's night-day conversion model was retrained on DETRAC data. Night dataset (26,168 images) extracted from DETRAC were used. The results showed that the training on the primitive data is highly effective (64.51%mAP) compared to the image changed from night to day, particularly on the car class (92%AP), bus (91%AP). This is the premise for the next studies and the basis to develop intelligent traffic monitoring systems.
机译：最近深入学习的增长已经开辟了车辆检测问题的许多机会。检测可见性差的物体正在捕捉科学家的关注。在这项研究中，我们选择夜晚作为挑战。我们对Yolov4方法进行了培训和评估，与图像预处理方法组合：Gamma，Constgan的夜间转换模型在DetRAC数据上培训。使用从DetRAC提取的夜间数据集（26,168个图像）。结果表明，与夜间到日期的图像相比，原始数据的训练是高效的（64.51％的地图），特别是在汽车课上（92％AP），总线（91％AP）。这是下一项研究的前提和开发智能交通监控系统的基础。

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