目的螺栓是输电线路中数量最多的紧固件,一旦出现缺陷就会影响电力系统的稳定运行。针对螺栓缺陷自动检测中存在的类内多样性和类间相似性挑战,提出了一种融合先验信息和特征约束的Faster R-CNN(faster regions with convolutional neur...目的螺栓是输电线路中数量最多的紧固件,一旦出现缺陷就会影响电力系统的稳定运行。针对螺栓缺陷自动检测中存在的类内多样性和类间相似性挑战,提出了一种融合先验信息和特征约束的Faster R-CNN(faster regions with convolutional neural network)模型训练方法。方法在航拍巡检图像预处理阶段,设计了基于先验信息的感兴趣区域提取算法,能够提取被识别目标的上下文区域,从而减少模型训练阶段的数据量,帮助模型在训练阶段关注重点区域,提高其特征提取能力。在模型训练阶段,首先通过费舍尔损失约束Faster R-CNN模型的输出特征生成,使样本特征具有较小的类内距离和较大的类间间隔;然后采用K近邻算法处理样本特征得到K近邻概率,将其作为难易样本的指示以引导模型后续更加关注难样本。结果在真实航拍巡检图像构建的螺栓数据集上进行测试,与基线模型相比,本文模型使螺栓识别的平均精度均值(mean average precision,mAP)提高了6.4%,其中正常螺栓识别的平均精度(average precision,AP)提高了0.9%,缺陷螺栓识别的平均精度提高了12%。结论提出的融合先验信息和特征约束的输电杆塔螺栓缺陷检测方法在缺陷螺栓识别上获得了良好的效果,为实现输电线路螺栓缺陷的自动检测奠定了良好的基础。展开更多
针对小目标水漂垃圾形态多变、分辨率低且信息有限,导致检测效果不理想的问题,提出一种改进的Faster-RCNN(Faster Regions with Convolutional Neural Network)水漂垃圾检测算法MP-Faster-RCNN(Faster-RCNN with Multi-scale feature an...针对小目标水漂垃圾形态多变、分辨率低且信息有限,导致检测效果不理想的问题,提出一种改进的Faster-RCNN(Faster Regions with Convolutional Neural Network)水漂垃圾检测算法MP-Faster-RCNN(Faster-RCNN with Multi-scale feature and Polarized self-attention)。首先,建立黄河兰州段小目标水漂垃圾数据集,将空洞卷积结合ResNet-50代替原来的VGG-16(Visual Geometry Group 16)作为主干特征提取网络,扩大感受野以提取更多小目标特征;其次,在区域生成网络(RPN)利用多尺度特征,设置3×3和1×1的两层卷积,补偿单一滑动窗口造成的特征丢失;最后,在RPN前加入极化自注意力,进一步利用多尺度和通道特征提取更细粒度的多尺度空间信息和通道间依赖关系,生成具有全局特征的特征图,实现更精确的目标框定位。实验结果表明,MP-Faster-RCNN能有效提高水漂垃圾检测精度,与原始Faster-RCNN相比,平均精度均值(mAP)提高了6.37个百分点,模型大小从521 MB降到了108 MB,且在同一训练批次下收敛更快。展开更多
Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these ...Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.展开更多
文章基于改进更快的区域卷积神经网络(Faster Region Convolutional Neural Networks,Faster R-CNN)模型,提出了一种行人识别系统设计。介绍了计算机视觉常用技术手段与方法、通行检测步骤,分析了主流的算法优缺点,利用深度学习方法提...文章基于改进更快的区域卷积神经网络(Faster Region Convolutional Neural Networks,Faster R-CNN)模型,提出了一种行人识别系统设计。介绍了计算机视觉常用技术手段与方法、通行检测步骤,分析了主流的算法优缺点,利用深度学习方法提取图像特征,然后使用改进Faster R-CNN模型进行目标检测。在改进Faster R-CNN模型中,采用了自适应尺度池化和增强的感兴趣区域(Region of Interest,RoI)池化技术,可以提高模型检测精度和速度。展开更多
文摘目的螺栓是输电线路中数量最多的紧固件,一旦出现缺陷就会影响电力系统的稳定运行。针对螺栓缺陷自动检测中存在的类内多样性和类间相似性挑战,提出了一种融合先验信息和特征约束的Faster R-CNN(faster regions with convolutional neural network)模型训练方法。方法在航拍巡检图像预处理阶段,设计了基于先验信息的感兴趣区域提取算法,能够提取被识别目标的上下文区域,从而减少模型训练阶段的数据量,帮助模型在训练阶段关注重点区域,提高其特征提取能力。在模型训练阶段,首先通过费舍尔损失约束Faster R-CNN模型的输出特征生成,使样本特征具有较小的类内距离和较大的类间间隔;然后采用K近邻算法处理样本特征得到K近邻概率,将其作为难易样本的指示以引导模型后续更加关注难样本。结果在真实航拍巡检图像构建的螺栓数据集上进行测试,与基线模型相比,本文模型使螺栓识别的平均精度均值(mean average precision,mAP)提高了6.4%,其中正常螺栓识别的平均精度(average precision,AP)提高了0.9%,缺陷螺栓识别的平均精度提高了12%。结论提出的融合先验信息和特征约束的输电杆塔螺栓缺陷检测方法在缺陷螺栓识别上获得了良好的效果,为实现输电线路螺栓缺陷的自动检测奠定了良好的基础。
文摘针对小目标水漂垃圾形态多变、分辨率低且信息有限,导致检测效果不理想的问题,提出一种改进的Faster-RCNN(Faster Regions with Convolutional Neural Network)水漂垃圾检测算法MP-Faster-RCNN(Faster-RCNN with Multi-scale feature and Polarized self-attention)。首先,建立黄河兰州段小目标水漂垃圾数据集,将空洞卷积结合ResNet-50代替原来的VGG-16(Visual Geometry Group 16)作为主干特征提取网络,扩大感受野以提取更多小目标特征;其次,在区域生成网络(RPN)利用多尺度特征,设置3×3和1×1的两层卷积,补偿单一滑动窗口造成的特征丢失;最后,在RPN前加入极化自注意力,进一步利用多尺度和通道特征提取更细粒度的多尺度空间信息和通道间依赖关系,生成具有全局特征的特征图,实现更精确的目标框定位。实验结果表明,MP-Faster-RCNN能有效提高水漂垃圾检测精度,与原始Faster-RCNN相比,平均精度均值(mAP)提高了6.37个百分点,模型大小从521 MB降到了108 MB,且在同一训练批次下收敛更快。
基金Supported by the National Key R&D Program of China(No.2017YFC1405600)the National Natural Science Foundation of China(Nos.42076197,41576032)the Major Program for the International Cooperation of the Chinese Academy of Sciences(No.133337KYSB20160002)。
文摘Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.
文摘文章基于改进更快的区域卷积神经网络(Faster Region Convolutional Neural Networks,Faster R-CNN)模型,提出了一种行人识别系统设计。介绍了计算机视觉常用技术手段与方法、通行检测步骤,分析了主流的算法优缺点,利用深度学习方法提取图像特征,然后使用改进Faster R-CNN模型进行目标检测。在改进Faster R-CNN模型中,采用了自适应尺度池化和增强的感兴趣区域(Region of Interest,RoI)池化技术,可以提高模型检测精度和速度。
