Lightweight Underwater Target Detection Algorithm Based on YOLOv8n

Abstract

To address the challenges in underwater target detection, such as complex environments, image blurring, and high model parameter counts and computational complexity, an improved lightweight detection algorithm, RDL-YOLO, is proposed. This algorithm incorporates multiple optimizations based on the YOLOv8n model. The introduction of the RFAConv module optimizes the backbone network, enhancing feature extraction capabilities under complex backgrounds. The DySample dynamic upsampling module is used to effectively improve the model’s ability to capture edge information. A lightweight detection head based on shared convolutions is designed to achieve model lightweighting. The combination of the normalized wasserstein distance (NWD) loss function and CIoU loss improves the detection accuracy for small targets. Experimental results on the UPRC (Underwater Robot Prototype Competition) and RUOD (Real-World Underwater Object Detection) datasets show that the improved algorithm achieves an average precision (mAP) increase of 1.4% and 1.0%, respectively, while reducing parameter count and computational complexity by 19.3% and 14.8%. Compared to other state-of-the-art underwater target detection algorithms, the proposed RDL-YOLO not only improves detection accuracy but also achieves model lightweighting, demonstrating superior applicability in resource-constrained underwater environments.