# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
## 1 Introduction  
To improve road safety, 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 Intelligent Transport System has initiated a pilot project with the aim of automatically reporting and forecasting the current and future availability of parking spaces at rest areas in the Barents region. The pilot project ran from January to April 2021 in two rest areas, one in northern Norway and one in northern Sweden. A crucial part of this pilot project was the detection of heavy goods vehicles on images from a thermal network camera. In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1, 2, 3, 4, 5, 6, 7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11, 12, 13]; vehicle classification [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]; vehicle counting system on streets and highways [27, 28, 29, 30, 31]; parking spot detection from the point of view of the car for parking assistants [32, 33]; and parking spot monitoring [34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]. Most of the previous studies about parking spot monitoring use data from parking areas for passenger cars which have marked parking spots for each car or in settings in which the cars park in one row along a street [34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. This differs from the settings of the two rest areas used in our study, which are primarily used by heavy goodsvehicles. Passenger cars also pass through these rest areas, but only a few and generally only for brief stops. In winter, the markings of the parking spots are covered by snow and ice and therefore not visible, so heavy goods vehicles do not park in a line or in marked parking spots. This leads to several challenges in detecting heavy goods vehicles: the vehicles face the camera from different angles (front, back, side); so the size of the vehicles differs depending on the distance to the camera, and there is a high overlap of vehicles on the camera image and many vehicles are cut off, so see Figure 1 for examples.  
In this paper, we used the latest version of the You Only Look Once (YOLO) object detection algorithm [50] to detect vehicles. As computer vision practitioners, our focus was on the application of the algorithm, data acquisition and data annotation. The remainder of this paper is organised as follows. Section 2 describes the selection of the algorithm and the dataset. The training and results are described in Section 3, ideas for further improvement and development are discussed in Section 4, followed by a conclusion in Section 5.