Adaptive Head Pruning for Attention Mechanism in the Maritime Domain

In this paper, we introduce a novel and synergistic approach that combines attention mechanisms, LVENet for image visibility enhancement, and a tailored head pruning method for Multi-Head Self Attention (MHSA) models, specifically engineered for AACN (Attention Augmented Convolutional Network) and BoTNet (Bottleneck Transformers). The integration of these techniques aims to comprehensively address the challenges associated with object detection in the maritime domain. The attention mechanism selectively emphasizes critical areas of the image, Low-Visibility Enhancement Network (LVENet) enhances visibility under challenging conditions, and the head pruning method optimizes model efficiency and simplicity. Employing meticulous selection and evaluation, our approach achieves precise head pruning without compromising detection performance. Validation using common and maritime datasets underscores the effectiveness of our approach. The results showcase a substantial reduction in epoch time by over 30%, while enhancing accuracy, improving computational efficiency, and streamlining model complexity. This innovation facilitates deployment in challenging maritime scenarios.