水力发电学报2023，Vol.42Issue(12)：146-158,13.DOI:10.11660/slfdxb.20231214
混凝土坝面交叉作业安全风险智能识别方法
Intelligent identification method for safety risks in cross operation on concrete dam surface
摘要
Abstract
To identify the operational risk of dam construction quickly and accurately,we develop an intelligent risk identification method(YOLO-CDSRI)for the safety risks of cross operation on a concrete dam surface,based on the YOOv8 network and considering the characteristics of complex scenes of such operation.First,a backbone network is constructed using a Cross Stage Partial Network(CSPNet)module and a Spatial Pyramid Pooling-Fast(SPPF)module to enhance the model's situational awareness of safety risks shown in the construction site images.Then,to address the issues of misidentification and missed identification of small target safety risks,this method adopts the Bidirectional Feature Pyramid Network(BiFPN).And using bidirectional cross scale connections and weighted feature fusion,it strengthens information coupling between the risk features and enhances the model's attention to small target safety risks.Finally,the method evaluates the quality of the anchor box via an"outlier"to avoid the excessive influence of geometric factors of the label box on the model,by using Wise-IoU as the boundary box regression loss function and combining with the dynamic non-monotonic focusing mechanism.Results show that after 500 iterations of training,the comprehensive performance of YOLO-CDSRI is superior to YOLOv5s,SSD,and Faster-RCNN models,thus promoting intelligent identification of the safety risks in cross operation on concrete dam surfaces.关键词
混凝土坝/交叉作业/复杂场景/安全风险/智能识别Key words
concrete dams/cross operations/complex scenarios/safety risks/intelligent identification分类
建筑与水利引用本文复制引用
曹坤煜,陈述,陈云,孙孟文,聂本武..混凝土坝面交叉作业安全风险智能识别方法[J].水力发电学报,2023,42(12):146-158,13.基金项目
国家自然科学基金(52079073 （）
52209163) （）
