An improved YOLOv8 model enhanced with detail and global features for underwater object detection

Abstract

Underwater object detection is significant for the practical research of mastering existing marine biological resources. In response to the challenges posed by complex underwater environments such as water scattering and variations in object scales, researchers have developed YOLOv8 for object detection, driven by the rising popularity and iteration of deep learning. Building upon this model, we propose an enhanced underwater object detection model named YOLOv8-DGF. Firstly, we replace the convolutional layers of Spatial Pyramid Pooling Fusion (SPPF) with Invertible Neural Networks to further augment the fusion capacity of detailed features, facilitating the preservation of pivotal information while mitigating the impact of noise. Additionally, we introduce a global attention mechanism into Convolution to Fully Connected (C2f), which weights the input features, thereby emphasizing or suppressing feature information from different locations. Through our ‘Detail to Global’ strategy, the model achieved mAP@0.5 scores of 87.7% and 84.8% on the RUOD and URPC2020 datasets, respectively, with improved processing speed. Extensive ablation experiments on the Pascal VOC dataset demonstrate that YOLOv8-DGF outperforms other methods, achieving the best overall performance.