Empowering Search and Rescue Operations with Big Data Technology: A Comprehensive Study of YOLOv8 Transfer Learning for Transportation Safety

In this research work, we demonstrate the important role of object detection technology and how to optimize it for elevating the efficiency of accident rescue missions in the maritime transportation industry. In this context, the use of unmanned aerial vehicles in search and rescue missions is a promising research topic. However, the processing power limitations and the lack of data focused on sea operations present some challenges in this area. The mix of synthetic and real data during the training and even the total replacement of real data with virtual generated ones can lead to a good and flexible solution for the dataset challenges. Another strategy for dealing with the lack of data is the use of transfer learning for leveraging the knowledge in a domain with an abundance of data when compared to a new domain of interest. In this work, the use of transfer learning and synthetic and real mixed datasets is explored for the field of search and rescue. The YOLOv8 is trained in different configurations of regular learning and transfer learning, with fine-tuning and 4 and 7 frozen layers, using both synthetic and real data. Finally, the models and the set of data are evaluated based on mAP50-95 showing some possible reasons for a performance difference between real and synthetic data in the training process.