计算机与数字工程2024，Vol.52Issue(3)：864-872,9.DOI:10.3969/j.issn.1672-9722.2024.03.039
基于深度学习的油田在线视频目标检测
Research and Application of Online Video Target Detection Based on Deep Learning
张千 1梁鸿 1童彦淇 1李洋1
作者信息
- 1. 中国石油大学(华东)计算机科学与技术学院 青岛 266580
- 折叠
摘要
Abstract
The complex and ever-changing background of the oil field,coupled with high camera suspension,results in a smaller proportion of objects in the monitoring image,increasing the difficulty of detection.Starting from the actual oilfield scenar-io,the low accuracy of SSD algorithm in detecting small targets is deeply studied and improved.The RP-SSD algorithm is proposed by adding an upsampling module and a prediction module in the feature pyramid to better fuse the feature maps generated by the front and back convolutional layers.Hollow convolution is used to expand the receptive field of the front convolutional layers,im-proving the accuracy of small target detection.Pascal VOC is used to validate the effectiveness of the improved algorithm,and fast R-CNN,SSD300,and DSSD321 are selected as control experiments.The experimental results show that RP-SSD significantly im-proves its performance in small object detection and can meet the requirements of real-time detection.关键词
小目标检测/特征金字塔/残差网络/空洞卷积Key words
small target detection/feature pyramid/residual network/void convolution分类
信息技术与安全科学引用本文复制引用
张千,梁鸿,童彦淇,李洋..基于深度学习的油田在线视频目标检测[J].计算机与数字工程,2024,52(3):864-872,9.
