Application and enhancement of deep learning in infrared image recognition of marine surface vessel targets
TL;DR
An improved model based on YOLOv8n network, which aims to the issue of high false alarm rate caused by marine surface clutter in the recognition task of infrared image of vessel target, is proposed and achieved a balance between robustness and lightweight design.
Abstract
In this study, we proposed an improved model based on YOLOv8n network, which aims to the issue of high false alarm rate caused by marine surface clutter in the recognition task of infrared image of vessel target. By integrating StarNet--a backbone network that based on “star operation”, and adding deformable attention mechanism (DAT), our model improved its ability in separating noise from structured signal, and realized data-driven adaptive focus. Compared to the original, our Experiment based on public dataset show that the model our putted up reduces false alarm rate by 2%. At the same time, this achievement comes with a reduction computational load and numbers of parameters—by 17.5% and 21.0%. we convinced it achieved a balance between robustness and lightweight design. This study provides an effective method for the task of detecting infrared vessel targets under complex clutter backgrounds. Moreover, its lightweight design is beneficial to the practical deployment on devices that lack of computing power.