西安石油大学学报（自然科学版）2024，Vol.39Issue(2)：120-127,8.DOI:10.3969/j.issn.1673-064X.2024.02.015
基于改进的YOLOv7油田井场压力表小目标识别
Identification of Small Target Pressure Gauges in Oilfield Well Sites Based on Improved YOLOv7
摘要
Abstract
In order to solve the problems of difficult feature extraction and low detection rate of small target pressure gauges on oilfield well site under different lighting conditions,a real-time well site small target pressure gauge detection algorithm based on an improved YOLOv7 is proposed.Firstly,a simple linear combination of global max pooling and global min pooling is used to generate an adaptive pooling layer,and a lightweight module is used to generate an SPEMattention module,which is added to the YOLOv7 backbone network to improve the network's feature extraction of small target pressure gauges under different lighting conditions.Secondly,SiLU function is replaced by modulus activation function to reduce dead nodes,solve the problem of gradient vanishing,and improve the model's generali-zation ability.Finally,the CIoU loss function in the original YOLOv7 is replaced with Wise-IoU to optimize the loss function,and the harmful gradients generated by low-quality examples is reduced by using a gradient gain allocation strategy to focus on the prediction and regression of ordinary quality anchor boxes.The experimental results show that compared to the original YOLOv7 algorithm,the im-proved algorithm improves accuracy by 1.07%and recall rate by 2.08%.At the same time,it also outperforms the detection results of Faster RCNN,SSD,and YOLOv3 algorithms,and can effectively meet the detection requirements of small target pressure gauges in oil-field well sites,with strong engineering practical significance.关键词
智能井场/压力表/小目标检测/YOLOv7/SPEM注意力模块/模量激活函数Key words
intelligent well site/pressure gauge/small target detection/YOLOv7/SPEMattention module/modulus activation function分类
信息技术与安全科学引用本文复制引用
白俊卿,常文文,程国建,黄小朋..基于改进的YOLOv7油田井场压力表小目标识别[J].西安石油大学学报（自然科学版）,2024,39(2):120-127,8.基金项目
陕西省自然科学基金基础研究计划"基于超网络的低空无人机视觉实时图像语义分割算法研究"(2023-JC-YB-601) （2023-JC-YB-601）
