Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

• Published in: Algorithms
• Main Authors: Margrit Kasper-Eulaers, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland, Per Egil Kummervold
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: object detection; YOLOv5; CNNs; vehicle detection; Industrial engineering. Management engineering; T55.4-60.8; Electronic computers. Computer science; QA75.5-76.95; polar night
• Online Access: https://doi.org/10.3390/a14040114; https://doaj.org/article/bebba01b249d47448e7e568a92e33681

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.