YOLOv8-UC: An Improved YOLOv8-Based Underwater Object Detection Algorithm

Underwater object detection technology is widely used in fields such as ocean exploration. However, due to the complex underwater environment, issues like light attenuation and scattering lead to low detection accuracy, which fails to meet the requirements. To address these issues, we propose an improved YOLOv8n-based model called YOLOv8-UC. This model incorporates a modified Dilation-wise Residual (DWR) C2f module to enhance the ability to extract features from the network’s high-level expandable receptive fields. It also integrates the Large Separable Kernel Attention (LSKA) module with the SPPF of YOLOv8 to enhance multi-scale feature extraction capabilities, reducing the loss of details. To solve the problem of redundant parameters and computational load in the detection head, the original detection head is replaced with a shared parameter structure, and RepConv is introduced. Additionally, the Inner-SIoU loss function is improved by using auxiliary boundaries at different scales to accelerate bounding box regression and improve detection accuracy. Experimental results show that the designed YOLOv8-UC achieves an mAP@0.5 of 79.3%, with a 6.9% increase in detection accuracy (P) and a 5.9% increase in precision (mAP@0.5) compared to YOLOv8n, demonstrating the effectiveness and application prospects of this method.