Multiple seam interactions are a major source of ground instability in several U.S.coalfields.Empirical methods are well suited for this problem,because while the mechanics multiple seam interactions are very complex ...Multiple seam interactions are a major source of ground instability in several U.S.coalfields.Empirical methods are well suited for this problem,because while the mechanics multiple seam interactions are very complex and poorly understood,many mining case histories are available for analysis.This study makes use of an updated database that includes 356 multiseam case histories,including 67 unsuccessful designs.The paper describes in detail the process used to design the study,collect the data,conduct the statistical analysis,and develop the quantitative model.The model can be used for mine planning in multiple seam situations,and has been made available as a module within the Analysis of Coal Pillar Stability(ACPS)computer program.展开更多
在智能交通系统和自动驾驶等领域中,车辆目标检测的准确性和鲁棒性至关重要。然而,现有目标检测模型的性能在复杂交通场景中显著下降,难以满足实际应用需求。为此,提出了一种基于“你只看一次”(You Only Look Once,YOLO)改进的车辆目...在智能交通系统和自动驾驶等领域中,车辆目标检测的准确性和鲁棒性至关重要。然而,现有目标检测模型的性能在复杂交通场景中显著下降,难以满足实际应用需求。为此,提出了一种基于“你只看一次”(You Only Look Once,YOLO)改进的车辆目标检测模型——YOLOv5-MultiSEAM。首先,采用交并比(intersection over union,IoU)距离替代欧式距离作为K-means算法的评价标准,优化了锚框聚类算法;其次,在YOLOv5模型的检测头部引入了由YOLO-Face提出的空间增强注意力模块(spatially enhanced attention module,SEAM),有效提升了遮挡情况下的检测性能;最后,采用Focaler-IoU Loss替代传统定位损失函数,实现了更为精准的定位和更快速的收敛。在城市事故检测与跟踪(urban accident detection and eracking,UA-DETRAC)公开数据集上的实验结果表明,该模型在车辆目标检测领域具有效性和优越性。展开更多
文摘Multiple seam interactions are a major source of ground instability in several U.S.coalfields.Empirical methods are well suited for this problem,because while the mechanics multiple seam interactions are very complex and poorly understood,many mining case histories are available for analysis.This study makes use of an updated database that includes 356 multiseam case histories,including 67 unsuccessful designs.The paper describes in detail the process used to design the study,collect the data,conduct the statistical analysis,and develop the quantitative model.The model can be used for mine planning in multiple seam situations,and has been made available as a module within the Analysis of Coal Pillar Stability(ACPS)computer program.
文摘在智能交通系统和自动驾驶等领域中,车辆目标检测的准确性和鲁棒性至关重要。然而,现有目标检测模型的性能在复杂交通场景中显著下降,难以满足实际应用需求。为此,提出了一种基于“你只看一次”(You Only Look Once,YOLO)改进的车辆目标检测模型——YOLOv5-MultiSEAM。首先,采用交并比(intersection over union,IoU)距离替代欧式距离作为K-means算法的评价标准,优化了锚框聚类算法;其次,在YOLOv5模型的检测头部引入了由YOLO-Face提出的空间增强注意力模块(spatially enhanced attention module,SEAM),有效提升了遮挡情况下的检测性能;最后,采用Focaler-IoU Loss替代传统定位损失函数,实现了更为精准的定位和更快速的收敛。在城市事故检测与跟踪(urban accident detection and eracking,UA-DETRAC)公开数据集上的实验结果表明,该模型在车辆目标检测领域具有效性和优越性。
