## 7 Vision-Based Object Recognition in a Vegetation Environment  
Installed cameras allow the vehicle's operating environment to be recorded from different perspectives. Additionally, a thermal camera can distinguish certain objects from the background due to infrared radiation. In order for vehicle cameras to perceive the surroundings, it is important to define their location at the design stage of the vehicle. The main camera was installed close to the windscreen, imitating the driver's perception. Unfortunately, the installed Velodyne LiDAR obscures the camera view, extending the blind zone, making impossible to observe the road directly in front of the vehicle (Figure 12). Daylight cameras capture an image with more details of the surroundings, such as concrete fence posts (Figure 12d), vegetation, and a snow-covered road. The image from the thermal camera basically consists of three areas: the sky (Figure 12a), the environment of the vehicle with a similar temperature (Figure 12b), and the warm parts of the vehicle (Figure 12c). Obstacle detection using a deterministic algorithm or an artificial neural network requires objects to be distinguished against the background from the data recorded by the sensor. The lighting and weather conditions can significantly affect the quality of images obtained from daylight cameras. In the case of the winter season, which is characterized by white snow cover, image quality can be improved in certain scenarios, such as to detect the contrast between the snow and the surroundings. Obstacles that are located on a similarly colored background (or surrounded by dense vegetation) pose difficulties for the recognition process based on a daylight camera stream (Figure 13a,b). Figure 11: Comparison of 3D point-cloud maps generated by LiDAR: Ouster OS0-32 (**a**) and Velodyne VLP-16 (**b**). During other seasons, such as summer, the background of the environment masks objects more effectively, so lighting conditions can directly the algorithms being used. A thermal camera is more resistant to changing illumination and environmental conditions. During imaging of the operational environment, the vision sensor matches the shades of gray to the measured maximum and minimum temperatures. As a result, regardless of the ambient temperature, warmer or cooler objects are more clearly visible. In our test scenario, a lower ambient temperature and warmer objects are indicated (Figure 14a-c). A disadvantage is that objects with a temperature similar to the ambient temperature are more difficult to distinguish. Additionally, thermal cameras are sensitive to environmental factors such as wind, rain, and fog, which can reduce their effectiveness. Therefore, it is important to carefully consider the use of thermal cameras and take into account the specific environmental conditions in which they will be used. Figure 12: Views from the daylight thermal camera with marked objects: sky(**a**), trees (**b**), vehiclehood (**c**) and fence post (**d**). Figure 13: Location of a suitcase (**a**), people (**b**), and barrel (**c**) in the vehicle’s operational environment. Distinguishing objects against the background in the operational environment is an important step in developing autonomous systems. Once the objects are detected and identified, deterministic algorithms or artificial neural networks can be implemented to perform specific tasks such as object tracking, obstacle avoidance, or path planning. The application of artificial neural networks requires advanced recording of a large amount of data to train and validate the network architecture. It is also important to ensure that the data are labelled correctly to learn accurate predictions or classifications. This requires images containing the typical objects in various environmental and lighting conditions. We evaluated object detection using the YOLO v5 neural network based on data recorded from cameras [29].