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

Margrit Kasper-Eulaers,Nico Hahn,Stian Berger,Per Egil Kummervold,Øystein Myrland,Tom Sebulonsen

doi:10.3390/a14040114

Abstract

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.

Highlights

YOLOv5 was pre-trained on the Common Objects in Context (COCO) dataset, an extensive dataset for object recognition, segmentation, and labelling
Changes to the physical data collection cannot be influenced. As this is a pilot project running on only two rest areas, there is the possibility of changing the physical setup for data collection if more rest areas are added
We already showed that when analysing images from small angle cameras to detect objects that occur in groups and have a high number of overlaps and cut-offs, the model can be improved by detecting certain characteristic features instead of the whole object

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Drivers of heavy goods vehicles must comply with strict rules regarding driving time and rest periods. Due to these regulations and contractual delivery agreements, heavy goods vehicle traffic is highly schedule driven. Arriving at a crowded rest area after long journeys leads to drivers exceeding the permitted driving time or having to rest outside of designated areas. As both can lead to increased traffic risk, the Barents

Methods

Findings

Discussion

Conclusion