基于YOLOv5的靠船排场景下安全措施检测  

Detection of Safety Measures in Berthing Row Scenario Based on YOLOv5

作　　者：崔剑勇 宫法明[1] 袁向兵[2] CUI Jian-Yong;GONG Fa-Ming;YUAN Xiang-Bing(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China;SINOPEC Shengli Oilfield Branch Offshore Oil Production Plant,Dongying 257237,China)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,青岛266580 [2]中国石化胜利油田分公司海洋采油厂,东营257237

出　　处：《计算机系统应用》2023年第6期51-59,共9页Computer Systems & Applications

摘　　要：跳板在海上平台作业现场扮演着重要角色.未搭设跳板对作业现场造成了极大的安全隐患.为消除此隐患,提出一种靠船排场景下的跳板搭设检测方法.本方法分为3个部分:首先利用目标检测算法定位并标注目标;之后对标注目标区域进行边缘检测,提取其外接边缘;最终制定特定安全措施判别算法识别作业现场违规动作.本方法为解决小目标等检测问题,对YOLOv5进行改进,在特征提取以及特征融合时引入注意力模块,将均值平均精度由53.1%提高至54.5%的同时模型更加轻量化;为解决检测边缘粗犷问题,对边缘检测网络PiDiNet损失函数进行改进,相较于原网络,误检率由8.9%下降至5.4%.经过验证,利用该方法可以在有效时间内,检测出跳板是否正确搭设,准确率为91.5%.Ramps are crucial to offshore platforms,and their absence will cause great safety risks to operation sites.To eliminate such risks,this study proposes a detection method of ramp setting up in the berthing row scenario.The method is divided into three parts:firstly,using the object detection algorithm to locate and mark the target;then,extracting the external edge of the marked target area by edge detection;finally,formulating the specific safety measures discrimination algorithm to identify violations in the work site.To solve the detection problems of small targets,this method improves the YOLOv5 and introduces an attention module in feature extraction and feature fusion,which makes the model more lightweight while improving its mean average precision(mAP)from 53.1%to 54.5%.As to rough edge detection,the loss function of the edge detection network PiDiNet is improved.Compared with the original network,the false detection rate decreases from 8.9%to 5.4%.The verification results indicate that the method can be used to detect whether the ramp is set up correctly within the effective time with accuracy up to 91.5%.

关 键 词：目标检测 YOLOv5 注意力机制 边缘检测 安全措施检测 

分 类 号：U698[交通运输工程—港口、海岸及近海工程] TP391.41[交通运输工程—船舶与海洋工程]