Optimized YOLOv8 for Lightweight Floating Object Detection on Unmanned Surface Vehicles

Authors

  • Honghong Dong College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China
  • Dan Li College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China
  • Shuailong Zhang College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China
  • Binjie Li The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China
  • Xin Chen The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China
  • Xiaozhu Wu College of The Academy of Digital China, Fuzhou University, Fuzhou 350003, China; The College of Computer and Data Science, Fuzhou University, Fuzhou 350003, China

DOI:

https://doi.org/10.5755/j01.itc.54.3.38966

Keywords:

USV application, Object detection, Small object, YOLOv8s, Floating object detection

Abstract

Unmanned surface vehicles (USVs) are increasingly being applied in water environment protection and management. A primary function is recognizing and detecting floating objects in aquatic environments. However, water surface floating object detection from USVs faces challenges such as high scene complexity, including sunlight reflection and shoreline reflections, in addition to identifying small objects. To tackle these issues, this study presents an improved YOLOv8s method for water surface floating object detection, named PSP-YOLOv8s. Firstly, we integrated the original C2f module with the Polarized Self-Attention (PSA) mechanism to design the C2f-PSA structure, thereby improving the model's ability to extract features in intricate environments. Secondly, we add a detection head specialized for small objects by fusing deep and shallow features, which effectively reduces the miss rate for small objects. Meanwhile, the Partial Convolution (PConv) technique is used to reconstruct the detection head, making the model lightweight. Finally, the Wise-IoUv3(WIoUv3) loss function is introduced to mitigate the impact of low-quality anchor frames in complex environments. Experimental results demonstrate that PSP-YOLOv8s achieves improvements of 4.3% in AP, 3.8% in AP50, and a significant 12.9% in APS on the self-constructed USV-WSFO dataset. The model's parameters, computational overhead, and size were reduced by 8.1%, 4.2%, and 2.8%, respectively. The proposed model's generalization capability is further validated through experiments on the Orca dataset and field trials. This work extends the application of vision technology in USVs, providing significant support for water resource and ecosystem protection. Code is available at https://github.com/hongh07/PSP-YOLOv8s. 

Downloads

Published

2025-10-14

Issue

Section

Articles