Marine Organism Detection Based on Double Domains Augmentation and an Improved YOLOv7

Abstract

The existing object detection methods are facing significant challenges when applied to marine environments, such as underwater image degradation caused by absorption and scattering of light, and domain transfer in water bodies with different water qualities. In this letter, we propose a marine organism detection framework to improve the detection performance and the domain generalization performance. First, a double domains data augmentation is proposed. This method combines underwater image enhancement and water quality transfer to improve the domain diversity of the original dataset. Second, we utilize the self-attention operations and the convolution to improve the detection performance of the YOLOv7, fully utilizing the advantages of self-attention and convolutional computation. Meanwhile, this model uses SIoU loss to accelerate convergence speed and improve the regression. Experiments on the URPC2019 and URPC2020 datasets show that the proposed object detection method achieves a mean average precision of 82.3% and 83.6%, respectively, which is superior to all other methods used for comparison.