舰船目标检测, 高分辨率光学影像, 仿真样本, 深度学习, 生成对抗网络

Due to real-world constraints, the quantity of ship datasets derived from remote sensing data is substantially limited and can’t fulfill the extensive sample demands required for training deep learning algorithms. According to this problem, a high-quality synthesizing method for three-band optical high-resolution remote sensing images containing ship targets is introduced, which utilizes 3D models and generative adversarial networks with style transfer capabilities. Based on the constructed dataset, synthetic samples are generated and evaluated. The experiments indicate that the approach can synthesize images visually close to real images. Incorporating these synthetic samples into the training process of detection models results in an increase of 2.6% in mAP for Faster R-CNN and 2.3% for YOLOv5.

ship detection, high-resolution optical image, synthetic samples, deep learning, generative adversarial networks