Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
复制标题
DOI：
10.3390/a14040114
复制
发表时间：
2021-04-01
期刊：
影响因子：
2.3
通讯作者：
Kummervold, Per Egil
中科院分区：
文献类型：
--
作者：
Kasper-Eulaers, Margrit;Hahn, Nico;Kummervold, Per Egil
根据休息区停车位的可用性适当规划休息时间对于运输公司以及交通和道路管理部门来说是一个重要问题。我们提供了一个案例研究，说明如何实施 You Only Look Once (YOLO)v5 来检测冬季休息区的重型货车，以便实时预测停车位占用情况。冬季的雪天和极夜通常会给图像识别带来一些挑战，因此我们使用热感网络摄像机。由于这些图像通常具有大量重叠和截止的车辆，因此我们将迁移学习应用于 YOLOv5，以研究前舱和后舱是否适合重型货车识别的特征。我们的结果表明，经过训练的算法可以高置信度地检测重型货车的前舱，而检测后部似乎更困难，特别是当距离摄像机较远时。总之，我们首先展示了当冬季条件导致具有大量重叠和截止的具有挑战性的图像时，使用其前部和后部而不是整个车辆来检测重型货车的改进，其次，我们展示了热网络成像在车辆检测中的前景。
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.