机械与电子2025，Vol.43Issue(10)：18-25,33,9.
基于FACV-YOLOv10模型的油田作业人员异常行为检测
Abnormal Behavior Detection of Oilfield Operators Based on FACV-YOLOv10 Model
摘要
Abstract
To address the issue of frequent safety accidents caused by improper operations at oilfield sites,this paper proposes an improved YOLOv10 model(FACV-YOLOv10).To address the issues of large target scale changes and complex backgrounds in oilfield monitoring,Focaler CIoU is used instead of traditional loss functions to enhance the detection capability for small targets;AdditiveBlock and ConvGLU are combined to optimize the backbone network,achieving lightweight while enhancing feature extraction performance;in addition,replacing C2f and Conv in Neck with VoV-GSCSP and GSConv improves the de-tection accuracy of the network for multi-scale targets and enhances its robustness in complex scenes.The model is validated on a self-built oilfield dataset,mAP@50 and mAP@50∶95 achieved 92.7％and 67.2％respectively,maintaining stable performance under complex conditions such as night inspections and severe weather,demonstrating good application value.关键词
YOLOv10/小目标检测/损失函数/模型轻量化/AdditiveBlock-CGLUKey words
YOLOv10/small target detection/loss function/model lightweighting/AdditiveBlock-CGLU分类
计算机与自动化引用本文复制引用
吴攀超,王佳硕,王婷婷..基于FACV-YOLOv10模型的油田作业人员异常行为检测[J].机械与电子,2025,43(10):18-25,33,9.基金项目
国家自然科学基金资助项目(52474036) （52474036）
