Title: Improving maritime distress target detection through modelling and simulation with YOLOv5s and Next-ViT

Authors: Xinbo Chang; Kun Liu; Zhen Liu

Addresses: School of Automation, Qingdao University, Qingdao, Shandong, China ' Qingdao Campus, PLA Naval Aviation University, Qingdao, Shandong, China ' School of Automation, Qingdao University, Qingdao, Shandong, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, Shandong, China

Abstract: To additionally raise the accuracy of maritime distress target detection, an improved YOLOv5s model with Next-ViT is proposed through modelling and simulation. In this model, Next-ViT is applied to extract representations, followed by adopting a neck network with spatial context pyramid and Focal-GIoU loss to identify the targets. To validate its effectiveness, extensive experiments are conducted on the sub-dataset of the SeaDronesSee dataset. Contrasted to the primary YOLOv5s model, the proposed model has promoted recall, mAP_0.5 and mAP_0.5-0.95 by 9.2, 6.3 and 3.3 percentage points, respectively, demonstrating superior performance over existing models.

Keywords: YOLOv5s model; target detection; Next-ViT; spatial context pyramid.

DOI: 10.1504/IJSPM.2025.148287

International Journal of Simulation and Process Modelling, 2025 Vol.22 No.1/2, pp.18 - 28

Received: 22 Nov 2024
Accepted: 31 Mar 2025
Published online: 01 Sep 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article