摘要
为掌握水下海珍品分布情况,本文结合YOLOv5s算法和注意力机制,得到一种新的轻量化目标检测模型——SE-YOLO模型。实验结果显示,相较于原YOLOv5s模型,该模型的准确率提升了1.1%、召回率提升了0.7%,并且在设计对比实验的过程中,发现传统图像增强算法并不具备提升目标检测准确度的可能。由此可见,本文提出的改进模型符合轻量化模型标准并兼具检测准确度高的优点,能够很好地完成对水下海珍品资源评估的任务。
In order to master the distribution of underwater treasures,a new lightweight target detection model,SE-YOLO model,is obtained by combining YOLOv5s algorithm and attention mechanism.The experimental results show that compared with the original YOLOv5s model,the accuracy of the model is increased by 1.1% and the recall rate is increased by 0.7%.And in the process of designing the comparison experiment,it is found that the traditional image enhancement algorithm does not have the possibility to improve the accuracy of the target detection.It can be seen that the improved model proposed in this paper conforms to the lightweight model standard and has the advantages of high detection accuracy,and can well complete the task of evaluating underwater treasure resources.
作者
马志强
MA Zhiqiang(Dalian Ocean University,Dalian 116023,China)
出处
《现代信息科技》
2021年第18期80-85,共6页
Modern Information Technology
