针对现有混凝土构件裂缝人工检测操作不仅费时、费力,而且易出现错检、误检、漏检,以及部分位置难以开展检测的问题,提出一种基于深度学习YOLOX(You Only Look Once)算法的混凝土构件裂缝智能化检测方法;首先采集、整理包含各类混凝土...针对现有混凝土构件裂缝人工检测操作不仅费时、费力,而且易出现错检、误检、漏检,以及部分位置难以开展检测的问题,提出一种基于深度学习YOLOX(You Only Look Once)算法的混凝土构件裂缝智能化检测方法;首先采集、整理包含各类混凝土构件的典型裂缝图像,并通过图像数据增强建立Pascal VOC数据集,然后基于Facebook公司开发的深度学习框架Pytorch,利用数据集训练YOLOX算法,并进行裂缝识别和验证;将训练完成后YOLOX算法移植至搭载安卓系统的手机端,进行现场实时检测操作。结果表明:在迭代次数为700时,混凝土构件裂缝识别精度可达88.84%,能有效筛分混凝土构件表面裂缝,并排除其他干扰项,证明了所提出的方法对裂缝具有较高的识别精度和广泛的适用性;经试验测试,移植至手机端的YOLOX算法能在提升便携性的同时保证高效、准确的检测效果,具有良好的应用前景。展开更多
为减轻电力工作人员的巡检负担,实现变电站智能巡检,对变电站设备缺陷检测算法进行了研究。首先,利用数据增强方法对有限的初始数据集进行扩充,利用多种图像处理方法增加数据集的复杂度,生成考虑复杂光照环境的数据集;然后,采用自适应...为减轻电力工作人员的巡检负担,实现变电站智能巡检,对变电站设备缺陷检测算法进行了研究。首先,利用数据增强方法对有限的初始数据集进行扩充,利用多种图像处理方法增加数据集的复杂度,生成考虑复杂光照环境的数据集;然后,采用自适应空间特征融合(ASFF:Adaptively Spatial Feature Fusion)的方法缓解特征金字塔中不同尺度特征的不一致性问题,并引入Focal损失函数作为置信度损失函数以缓解正负样本不平衡的问题,利用改进的YOLOX-s(You Only Look Once X-s)网络模型设计了变电站缺陷检测算法;最后,将改进的YOLOX-s网络模型与其他深度学习算法的检测效果进行对比,实验结果表明,改进的YOLOX-s网络模型的综合检测效果较好,准确性和实时性均可以满足变电站设备缺陷检测任务。展开更多
针对现有X光安检图像中违禁物品检测精度低的问题,基于YOLOv5s(you only look once version 5 small)提出了一种改进的违禁物品检测算法。利用重参数思想设计了一种Rep模块以协助YOLOv5s主干网络提取更多特征信息,在不增加推理时间的基...针对现有X光安检图像中违禁物品检测精度低的问题,基于YOLOv5s(you only look once version 5 small)提出了一种改进的违禁物品检测算法。利用重参数思想设计了一种Rep模块以协助YOLOv5s主干网络提取更多特征信息,在不增加推理时间的基础上提高算法检测精度。同时,在YOLOv5s颈部的路径聚合网络中插入2个通道注意力机制压缩-激励模块,加强通道间的相关性,提高整体网络的检测效果。在SIXray数据集上的实验结果表明,在不增加检测时间的基础上,改进的YOLOv5s算法比原始算法在平均精度均值(mAP)、宏精确率(macro precision)、宏召回率(macro recall)和宏F1(macro-F1)这4个评价指标上分别提升了2.6、2.0、4.0和3.0个百分点。展开更多
The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system usi...The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.展开更多
文摘针对现有混凝土构件裂缝人工检测操作不仅费时、费力,而且易出现错检、误检、漏检,以及部分位置难以开展检测的问题,提出一种基于深度学习YOLOX(You Only Look Once)算法的混凝土构件裂缝智能化检测方法;首先采集、整理包含各类混凝土构件的典型裂缝图像,并通过图像数据增强建立Pascal VOC数据集,然后基于Facebook公司开发的深度学习框架Pytorch,利用数据集训练YOLOX算法,并进行裂缝识别和验证;将训练完成后YOLOX算法移植至搭载安卓系统的手机端,进行现场实时检测操作。结果表明:在迭代次数为700时,混凝土构件裂缝识别精度可达88.84%,能有效筛分混凝土构件表面裂缝,并排除其他干扰项,证明了所提出的方法对裂缝具有较高的识别精度和广泛的适用性;经试验测试,移植至手机端的YOLOX算法能在提升便携性的同时保证高效、准确的检测效果,具有良好的应用前景。
文摘为减轻电力工作人员的巡检负担,实现变电站智能巡检,对变电站设备缺陷检测算法进行了研究。首先,利用数据增强方法对有限的初始数据集进行扩充,利用多种图像处理方法增加数据集的复杂度,生成考虑复杂光照环境的数据集;然后,采用自适应空间特征融合(ASFF:Adaptively Spatial Feature Fusion)的方法缓解特征金字塔中不同尺度特征的不一致性问题,并引入Focal损失函数作为置信度损失函数以缓解正负样本不平衡的问题,利用改进的YOLOX-s(You Only Look Once X-s)网络模型设计了变电站缺陷检测算法;最后,将改进的YOLOX-s网络模型与其他深度学习算法的检测效果进行对比,实验结果表明,改进的YOLOX-s网络模型的综合检测效果较好,准确性和实时性均可以满足变电站设备缺陷检测任务。
文摘针对现有X光安检图像中违禁物品检测精度低的问题,基于YOLOv5s(you only look once version 5 small)提出了一种改进的违禁物品检测算法。利用重参数思想设计了一种Rep模块以协助YOLOv5s主干网络提取更多特征信息,在不增加推理时间的基础上提高算法检测精度。同时,在YOLOv5s颈部的路径聚合网络中插入2个通道注意力机制压缩-激励模块,加强通道间的相关性,提高整体网络的检测效果。在SIXray数据集上的实验结果表明,在不增加检测时间的基础上,改进的YOLOv5s算法比原始算法在平均精度均值(mAP)、宏精确率(macro precision)、宏召回率(macro recall)和宏F1(macro-F1)这4个评价指标上分别提升了2.6、2.0、4.0和3.0个百分点。
基金This work was supported by Suranaree University of Technology(SUT).The authors would also like to thank SUT Smart Transit and Thai AI for supporting the experimental and datasets.
文摘The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.
