In this study, we have explored the implementation of the YOLOv8(nano) model for the task of real-time detection of military objects for UAV companion computers. We have collected and merged different datasets from the open sources with clearly annotated classes such as tanks, armored vehicles, armored personnel carriers, etc. Additionally, datasets with civilian people and vehicles have been included to address ethical concerns. Because of the poor quality of original datasets, we developed a processing pipeline for proper data selecting, filtering, and augmentation. We trained the YOLOv8-nano model for 100 epochs. The default pre-trained on the COCO dataset YOLOv8-nano model initially achieved an mAP@0.5 of 0.305 and mAP@0.5-0.95 of 0.169 on our dataset. Our final YOLOv8-nano model achieves a mAP@0.5 of 73.61% and mAP@0.5-0.95 of 51.18%. We also evaluated our model using combat videos from FPV(First Person View) drones containing different military targets.