However, the created dataset did not have object distance annotations, and actually did not consider object distances at all, which is also the case in the majority of published AI-based OD methods in railways. To the best of the authors' knowledge, the only known published work that is based on a custom railway dataset with annotation of railway obstacles and associated distances is work included within the H2020 Shift2 Rail projects SMART [7] and SMART2 [8]. The SMART railway dataset was created using data recordings in several static and dynamic field tests that were conducted on different railway sites in Serbia, according to permissions issued by the Serbian Railway. As illustrated in Figure 1a,b, during the static field tests, several cameras of different type, RGB, thermal, night vision and SWIR, were mounted on a static test stand viewing the rail tracks, with different obstacles located at different distances from the cameras, such as pedestrians that were imitated by the members of the SMART/SMART2 consortiums. In dynamic field tests, the cameras were integrated into a sensors' housing mounted on the front profile of an operational locomotive (Figure 1c,d), as described in [9] for the case of SMART obstacle detection system. Two different versions of the integrated sensors' housing were developed and tested during the successive projects, SMART and SMART2. As illustrated in Figure 1c, the SMART sensors' housing was horizontal and placed under the headlights of the locomotive, while the SMART2 sensors' housing was vertical and located in the middle of the locomotive frontal profile (Figure 1d). The dynamic field tests were performed with an in-service train of the operator Serbia Cargo. The test length was 120 km on the Serbian part of the pan-European corridor X. All the tests were performed in the period November 2017-June 2022, under different illumination and weather conditions. During the field tests, approximately 14 h of video were recorded by the SMART/SMART2 cameras. The majority of video images contain rail tracks without obstacles on them. In the images with obstacles, both static and moving obstacles are present, including humans, vehicles, and animals. Objects (obstacles) in the dataset were labeled with a number of parameters, including information about the class of the objects, the bounding box information of the object and the ground truth distance of the object to the cameras. In addition, objects in the dataset have associated instance segmentation masks, created manually by the online Hasty annotation tool [10]. These  
Figure 1: SMART and SMART2 field tests for dataset generation: Test-stand with the SMART (**a**) and SMART2 (**b**) sensors viewing the rail tracks and an object (human) on the rail tracks; SMART (**c**) and SMART2 (**d**) vision sensors for obstacle detection integrated into sensors’ housing mounted on the frontal profile of a locomotive. instance segmentation masks were used by the authors in related works for annotating the images for training instance segmentation AI models [11]. For the data augmentation method presented in this paper, these objects' instance segmentation masks were used as described in Section 3. The SMART dataset has supported development of the SMART/SMART2 on-board OD system, which is based on using state-of-the art bounding box DL-based models for object detection, that are originally trained with a public dataset such as COCO [12]. In the course of development of the SMART on-board OD system, different DL-based methods were used [13] as object detectors. In the recent advanced developments of the SMART2 on-board system, CenterNet [14], as one of the fastest and most reliable real-time networks for the object detection task, has been used. Although achieved results with the SMART vision-based OD system are satisfactory and they outperform other vision-based OD systems, particularly with respect to the distance detection range [15], there is still a need for further improvement. The reason for this is the insufficient amount of relevant training data.