RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8

To address the challenge posed by the abundance of small objects with weak object features and little information in the images of underwater biomonitoring scenarios, and the added difficulty of recognizing these objects due to light absorption and scattering in the underwater environment, this study proposes an improved RHS-YOLOv8 (Ref-Dilated-HBFPN-SOB-YOLOv8). Firstly, a combination of hybrid inflated convolution and RefConv is used to redesign the lightweight Ref-Dilated convolution block, which reduces the model computation. Second, a new feature pyramid network fusion module, the Hybrid Bridge Feature Pyramid Network (HBFPN), is designed to fuse the deep features with the high-level features, as well as the features of the current layer, to improve the feature extraction capability for fuzzy objects. Third, Efficient Localization Attention (ELA) is added to reduce the interference of irrelevant factors on prediction. Fourth, an Involution module is introduced to effectively capture spatial long-range relationships and improve recognition accuracy. Finally, a small object detection branch is incorporated into the original architecture to enhance the model’s performance in detecting small objects. Experiments based on the DUO dataset show that RHS-YOLOv8 reduces 9.95% of computing power, while [email protected] and [email protected]:0.95 are improved by 2.54% and 4.31%, respectively. Compared with other cutting-edge underwater object detection algorithms, the present algorithm improves the detection accuracy while lightweighting the improvement, which effectively enhances the capability to detect small underwater objects.