基于改进YOLOv3目标检测算法的船舶运载货物自动识别研究
Research on Automatic Identification of Ship Cargo Based on Improved YOLOv3 Object Detection Algorithm
侯国佼 1孙荣 1肖圣魁 1李雯 1张栋2
作者信息
- 1. 长江三峡通航管理局,湖北 宜昌 443002
- 2. 湖南天下宽信息技术有限公司,湖南 长沙 410000
- 折叠
摘要
船舶货物自动识别高精度数据获取难,影响检测性能.该文利用弱监督至全监督框架,结合改进算法构建组合框架,平均识别精度达32.0%,定位精度达73.8%,高于对比方法.该框架在弱监督环境下表现优异,适用于船舶货物自动识别.
Abstract
The difficulty in obtaining high-precision data for automatic identification of ship cargo affects the detection performance.This study utilizes a weak supervision to full supervision framework combined with improved algorithms to construct a combined framework.The average recognition accuracy reaches 32.0%,and the positioning accuracy reaches 73.8%,which is higher than the comparison methods.This framework performs excellently in a weak supervision environment and is suitable for automatic identification of ship cargo.
关键词
YOLOv3/弱监督/船舶运载/候选区域Key words
YOLOv3/weak supervision/ship transportation/candidate region引用本文复制引用
出版年
2024
NETL
NSTL
万方数据