FMT-YOLOv8:Feature Fusion Algorithm For Marine Vessel Target Detection Based On Improved YOLOv8

Considering the challenges posed by low background contrast, varying target scales, high computational complexity of the detection model, and real-time detection limitations in marine environments, this paper proposes an algorithm for naval target detection based on an enhanced YOLOv8. Firstly, we introduce a novel feature-focused diffusion pyramid network (FDN) to enhance the representation of feature maps by integrating features from different layers and employing diffusion mechanisms to address the issues of low background contrast and diverse target scales. Secondly, we incorporate the AIFI attention mechanism to facilitate feature interaction, reducing computational redundancy and enhancing detection performance. Additionally, by drawing inspiration from StarNet’s approach, we have further enhanced the C2f module to effectively address issues pertaining to gradient vanishing. Finally, we propose a task-align dynamic detection head (TADH) for classification and location branching tasks that learns task interaction features from multiple convolutional layers. This enables us to obtain joint features while reducing model complexity and addressing target scale inconsistencies.