Impact of synthetic images in the training of neural networks for airborne vessel segmentation
Francisco Matilde, Gonçalo Charters Santos Cruz, Diogo Victor Bandeira da Silva · 2025
Synthetic images have appeared as a possible solution to the scarcity of real images for training neural networks. We investigate their use in the training of ship detectors in aerial imagery. We create a synthetic dataset through 2 distinct methods: modeling and rendering with Blender, and a learning-based approach with GauGAN. YOLACT++ was chosen as the detector and its performance was evaluated when synthetic images were included in its training. The results suggest that synthetic augmentations can improve a model's performance, under certain conditions. Adding synthetic data to large training sets degraded performance on the target domain, but significantly improved it on small datasets.