Modified CycleGAN based sonar image library construction

Due to the difficulty of obtaining sonar image datasets, many underwater works, such as underwater object detection and tracking, super-resolution of sonar images, cannot be carried out normally. So, the construction of sufficient sonar image library becomes an important prerequisite for many underwater research works. Inspired by the conversion research work of optical image and synthetic aperture radar (SAR) image, the construction of sonar image library based on CycleGAN model is proposed to synthesize sonar image with optical image and realize image style transfer from optical to sonar. By improving the loss function of CycleGAN network, the effect of sonar image synthesis is improved. The experimental results comparing with Pix2Pix image style transfer network show that the modified CycleGAN network has better image style transfer effect. Finally, the Mask RCNN detection network is trained with synthesized sonar images and tested with real sonar images. The model obtained from training can successfully detect the corresponding targets in the real sonar images, which further verifies the effectiveness of the construction method of sonar image library using optical images.