混凝土坝施工场景人-机-环多要素识别方法  

Recognition method for multi-elements in human-machineenvironment scenarios of concrete dam construction

作　　者：陈云[1,2] 涂宇轩[2] 陈述 晋良海[1,2] CHEN Yun;TU Yuxuan;CHEN Shu;JIN Lianghai(Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang 443002,China;College of Hydraulic&Environmental Engineering,China Three Gorges University,Yichang 443002,China)

机构地区：[1]三峡大学水电工程施工与管理湖北省重点实验室,湖北宜昌443002 [2]三峡大学水利与环境学院,湖北宜昌443002

出　　处：《水力发电学报》2024年第12期13-22,共10页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(52209163,52079073,52479127)。

摘　　要：混凝土坝施工空间狭窄、工序转换不断、人员-机械-环境(人-机-环)等要素繁多,而且存在要素遮挡、密集重叠、尺寸和方向各异等情况,导致传统机器视觉目标识别方法难以满足复杂施工现场目标识别要求。因此,本文提出混凝土坝施工场景人-机-环多要素识别的YOLOv5-SS新方法。通过插入CBAM注意力模块,改进目标检测器的性能,增强目标检测器对不同尺寸和位置的人-机-环要素敏感性;同时融入加权双向特征金字塔网络(BiFPN),使得目标检测器聚焦于实时人-机-环要素等关键图像信息。为验证所提方法识别能力,以混凝土拱坝施工现场的图像信息为基础数据集,通过对比YOLOv5-SS、YOLOv5和Faster R-CNN等模型,验证了所提方法能有效提高混凝土坝施工场景中各类目标的效率和精准度。For concrete dam construction,traditional computer vision target recognition methods are difficult to meet the requirements for intelligent detection in complex construction sites,as it involves narrow spaces,continuous process transitions,and various other elements such as personnel,machinery,and environment(human-machine-environment or HME).These elements often lead to occlusions,dense overlaps,and variations in size and orientation.This paper describes a new method,YOLOv5-SS,for recognition of the multiple elements in the HME scenarios of such construction.By integrating a CBAM attention module,this method improves the performance of the object detector and enhances its sensitivity to HME elements of different sizes and positions.And,it incorporates the weighted bidirectional feature pyramid network(BiFPN)to enable the object detector to focus on key image information related to real-time HME elements.To validate the recognition capability of this method,a dataset based on image information from a concrete arch dam construction site is used.Comparison of YOLOv5-SS with the YOLOv5 and Faster R-CNN models demonstrates it effectively improves the efficiency and accuracy of target detection in concrete dam construction scenarios.

关 键 词：混凝土坝 人-机-环 多要素识别 计算机视觉 YOLOv5-SS 

分 类 号：TV512[水利工程—水利水电工程]