# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
## 4 Discussion  
We see the greatest potential for improving performance in adjusting the physical data collection and in improving the data annotation.  
For most applications, changes to the physical data collection cannot be influenced. However, 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. Our recommendations for the setup are: to continue using thermal network cameras as it is not possible to read number plates or identify detailed human characteristics in their images and the data are automatically anonymised. Furthermore, the camera delivered usable images for all light and weather conditions that occurred during the project period.  
Figure 7: Images from the test dataset evaluated by the baseline model, YOLOv5, on the left and by the altered model, YOLOv5 trained with additional data from the rest area dataset, on the right.  
However, we suggest using a wider angle camera to capture more and more complete heavy goods vehicles, avoid obstacles in the camera's field of view and increase the resolution of the images.  
The three classes in the dataset used in this paper are very unbalanced. Cars are highly underrepresented in the dataset, reflecting the fact that the rest areas are mainly used by trucks. One strategy to deal with this is to train on only two classes, _truck_front_ and _truck_back_, or to emphasise the annotations for cars more by adding more images with cars. The performance in recognising cars could be increased by adding images with cars from other publicly available datasets. However, there are also only half as many images with the _truck_back_ label as with the _truck_front_ label. We assume that performance can be increased after collecting and labelling more images, especially by balancing the number of images from both resting areas and increasing the number of images in the two smaller classes, _car_ and _truck_back_.  
In addition, we suggest reviewing the data augmentation strategies and using a higher augmentation rate to benefit more from the positive effects of augmentation [59].  
One way to deal with a static obstacle, such as the pole located in the middle of the images of a rest area, could be to crop it out of the image, since, for example, a truck cabin with a removed obstacle has more features in common with cabins without obstacles than the cabin with an obstacle has with a cabin without an obstacle (Figure 8B is more similar to Figure 8C,D) than Figure 8A is to Figure 8C,D).  
Currently, heavy goods vehicles with both the cab and the rear outside the image, or which are obscured by other vehicles, are rarely detected by our algorithm. To get closer to the goal of detecting all heavy goods vehicles in the picture, we first propose to further specialise our current model. Instead of training it to detect cabins in frontal and side view, it could be trained to detect only in frontal view (windscreen, front lights and number plate facing the camera). Secondly, we propose to add an additional model to the analysis for recognition. The additional model could either detect other characteristic features of heavy goods vehicles that are easily visible from the side, such as wheels, or it could classify the images into categories indicating the number of heavy goods vehicles. Knowing how many of the individual features of a heavy goods vehicle are detected in an image enables us to combine this information to estimate the number of heavy goods vehicles in an image and enables us to predict occupancy rates.