针对浒苔目标检测的全局背景强化的位置蒸馏方法

Distillation method based on global background strengthen for Enteromorpha prolifera target detection

浒苔检测是目前海洋环境智能监测领域研究的重要课题之一。为了有效解决传统浒苔检测方法存在的训练样本需求大的问题,本文提出了一种全局背景强化的位置蒸馏模型(GBS-LD)。通过引入全局上下文模块和背景蒸馏损失分支,解决了原始位置蒸馏方法在建模背景特征上的不足,在复杂海洋环境下有效提高了浒苔检测系统的稳健性。在浒苔检测数据集中,本文模型具有较高的准确性和实时性,为海洋智能监测提供了重要参考。

The detection of Enteromorpha prolifera stands as a pivotal research area within the realm of intelligent marine environment monitoring.Addressing the challenge posed by the substantial training sample requirements inherent in conventional methods of Enteromorpha prolifera detection,this paper proposes GBS-LD model.By introducing a global context module and background distillation loss branch,the shortcomings of the original position distillation method in modeling background features are solved,effectively improving the robustness of detection system in complex marine environments.Our proposed model has achieved high accuracy and real-time performance in the dataset of Enteromorpha prolifera,providing important reference for intelligent monitoring of marine.