摘要
为提高混凝土坝面作业场景识别工作效率,提出了一种混凝土坝面作业场景智能识别方法(ResNet50-SEMSF)。将采集的坝面施工现场监控视频分割为图像,分析混凝土坝面作业人、机、料、环境等实体要素图像特征,界定坝面作业典型场景;以残差网络(ResNet50)为骨干网络结构,引入挤压激励(SE)注意力机制,关注不同通道间特征关系,提升坝面作业场景图像中多目标实体要素关键特征表达能力;融合下采样多尺度特征,保留坝面作业场景图像低级特征和高级语义信息,增强模型对图像不同层次特征的理解能力,克服尺度变化、目标变形等问题。对比分析其他3种卷积神经网络模型试验结果,使用梯度类激活映射(Grad-CAM)可视化方法,解释ResNet50-SEMSF模型对场景类别中实体要素信息的关注程度。结果表明:ResNet50-SEMSF识别效果明显优于ResNet50、MobileNetV2、VGG16等经典网络模型,表明ResNet50-SEMSF模型用于混凝土坝面作业场景智能识别的可行性,为混凝土坝面施工安全管理工作提供参考。
To improve the identification efficiency of concrete dam surface operation scenes,a new intelligent identification method(ResNet50-SEMSF)for typical scenes is developed.The collected monitoring video of the construction scenes is segmented into images,and their features-such as workers,machines,materials,environment,and other entity elements-are examined to define the typical scenes on a dam surface.With Residual Network 50 as the backbone network structure,a squeeze excitation attention mechanism is adopted to enhance the capability of expressing the key features of multi-target entity elements in the operation images.The down-sampling multi-scale features of an operation image are fused so as to retain its low-level features and high-level semantic information,enhance the model's capability of understanding the features at different levels,and overcome the difficulties in scale change and target deformation.With comparative analysis of the test results by other three convolutional neural network models,the Grad Class Activation Mapping visualisation method is used to illustrate the extent to which our new model focuses on information about the entity elements in the scene categories.The results show its recognition effect is significantly better than that of ResNet50,MobileNetV2 and VGG16 classical network models,characterising its feasibility and usefulness for concrete dam face operation in intelligent scene recognition and safety management.
作者
陈述
孙孟文
陈云
曹坤煜
李智
聂本武
CHEN Shu;SUN Mengwen;CHEN Yun;CAO Kunyu;LI Zhi;NIE Benwu(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;China Three Gorges Corporation,Wuhan 430010,China;National Energy Investment Corporation,Chengdu 610000,China)
出处
《水力发电学报》
CSCD
北大核心
2024年第1期99-108,共10页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(52209163
52079073)。
关键词
混凝土坝
坝面作业
深度学习
注意力机制
场景智能识别
concrete dam
dam surface operation
deep learning
attention mechanism
scene intelligent recognition
