Underwater image classification and object detection using shuffle convolutional channel enhancement

The complexity of the underwater environment poses significant challenges to underwater visual fields such as underwater object classification and detection, due to factors such as light attenuation, colour distortion, blurriness, noise, and complex underwater backgrounds. This paper proposes a local attention algorithm using Shuffle Convolutional Channel enhancement (SF-NN), which improves the accuracy of key feature identification in images.The SF-NN technique effectively addresses variations in the scale of visual entities and high-resolution issues, improving connectivity between windows and significantly enhancing the accuracy of underwater image classification,and it was verified on the ImageNet and FishNet datasets. Furthermore, by integrating the proposed algorithm as a feature extraction method into the Mask R-CNN framework, we effectively tackle problems such as object occlusion and deformation in complex underwater scenes, thereby improving the precision and robustness of the Mask R-CNN object detection algorithm.It has been validated on the COCO and TransCan datasets, demonstrating the significant advantages of the algorithm in improving underwater object detection performance through applications in different underwater environments and targets.