基于MSRCP与改进YOLOv5的雾天船舶检测
Ship Detection in Fog Based on MSRCP and Improved YOLOv5
李伟 1张雪 1单雄飞 1宁君1
作者信息
- 1. 大连海事大学航海学院,辽宁 大连 116026
- 折叠
摘要
针对海上雾天获取到的图像中小目标船舶识别效果低下、漏检率高等问题,提出了一种融合 MSRCP 算法的改进YOLOv5 模型.在输入端加入MSRCP算法对图像进行预处理,提高远处船舶的特征;采用改进k-means聚类方法设计先验框,加快模型收敛速度,使锚框和边界框更匹配;在网络部分采用了SoftPool池化替换原来的MaxPool池化,保留更多的图像特征,提高图像的检测精度.经实验,改进后的算法MAP值提高了12%,平均召回率提升了 16%,检测速度达到 40 帧/秒,能够在满足实时性检测的前提下,更好地完成对大雾天气下的船舶识别.
Abstract
Aiming at the problems of low recognition effect and high missed detection rate of small target ships in the images obtained in fog on the sea,this paper proposes an improved YOLOv5 model integrating the MSRCP algo-rithm.The MSRCP algorithm is added to the input to preprocess the image to improve the characteristics of distant ships.The improved k-means clustering method is used to design a priori box,which accelerates the convergence speed of the model and makes the anchor box and the boundary box match better.In the network part,Softpool pooling is used to replace the original Maxpool pooling,retain more image features and improve the detection accuracy of ima-ges.The map value of the improved algorithm is increased by 12%,the average recall rate is increased by 16%,and the detection speed is up to 40 frames/second.On the premise of meeting the real-time detection effect,it can better complete the ship recognition in foggy weather.
关键词
目标检测/船舶识别/雾天船舶检测Key words
Target detection/Ship recognition/Ship detection in fog引用本文复制引用
出版年
2024
NETL
NSTL
万方数据