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Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion

Computers, Materials & Continua 2023年第3期74卷 6485-6503页

作者： Jianwei Yuan Xinli Song huaijian pu Zhixiong Zheng Ziyang Niu College of Civil Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer&Communication Engineering Changsha University of Science and TechnologyChangsha410114China China Construction Fifth Engineering Bureau Co. Ltd.Changsha410004China

Regular inspection of bridge cracks is crucial to bridge maintenance and *** traditional manual crack detection methods are timeconsuming,dangerous and *** the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and ***,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network ***,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the ***,we introduce the parallel attention mechanism into the encoding and decoding *** attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various ***,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion *** research results are validated on the self-made *** experiments show that our method is more accurate than other *** intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,*** addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/*** results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.

关键词： Crack detection DeeplabV3+ parallel attention mechanism feature fusion

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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Computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang huaijian pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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悬浮液基高效减阻携砂压裂液的研发与应用

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油田化学 2022年第3期39卷 387-392,400页

作者：麦尔耶姆古丽·安外尔蒲迪翟怀建刘宽邬国栋余波金诚中国石油新疆油田分公司工程技术研究院新疆克拉玛依834000 四川光亚聚合物化工有限公司四川南充637500 西南石油大学新能源与材料学院四川成都610500 中国石油新疆油田分公司百口泉采油厂新疆克拉玛依834011

为解决压裂用聚合类乳液稠化剂不耐高盐以及悬浮液稠化剂稳定性差的问题,以抗盐缔合聚合物KFPY为基础,通过考察悬浮液稠化剂的稳定时间、本体黏度,优选了体系的稳定剂,优化了粉末稠化剂粒径以及比例,最终形成悬浮液稠化剂GAF-TE组成为:4... 详细信息

为解决压裂用聚合类乳液稠化剂不耐高盐以及悬浮液稠化剂稳定性差的问题,以抗盐缔合聚合物KFPY为基础,通过考察悬浮液稠化剂的稳定时间、本体黏度,优选了体系的稳定剂,优化了粉末稠化剂粒径以及比例,最终形成悬浮液稠化剂GAF-TE组成为:48.5%白油+1.5%乳化剂G10+5%有机改性膨润土+45%抗盐缔合稠化剂KFPY。在玛18井水水质下,对悬浮液稠化剂GAF-TE的溶解性能、增黏性能、减阻性能以及配制的滑溜水体系的各项性能进行了评价。结果表明,0.1%稠化剂GAF-TE溶解时间为18 s,黏度为2.19 m Pa.s,降阻率为76.8%;配制的滑溜水体系动态携砂能力强于普通的聚丙烯酰胺乳液,体系表面张力26.8 mN/m,与煤油间的界面张力0.96 mN/m,对岩心基质渗透率伤害率为6.97%,具有低伤害特性。该缔合型悬浮液稠化剂产品在新疆油田某区块进行现场应用,表现出较好的溶解性、降阻性能和携砂性能。

关键词：悬浮增稠剂长期稳定性免混配滑溜水动态携砂能力高效减阻

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