闸板阀是煤矿生产过程中控制水、煤流量的常用设备。由于成本、布线等原因,非关键闸板阀的开度检测并未纳入到矿井集中监控系统中。鉴于此,在已有的视频监控系统基础上,针对现有基于图像处理的闸板阀开度检测算法存在需要多模型训练、...闸板阀是煤矿生产过程中控制水、煤流量的常用设备。由于成本、布线等原因,非关键闸板阀的开度检测并未纳入到矿井集中监控系统中。鉴于此,在已有的视频监控系统基础上,针对现有基于图像处理的闸板阀开度检测算法存在需要多模型训练、多步检测、容错率低的问题,提出了一类以改进的YOLO-tiny(包括YOLOv3-tiny和YOLOv4-tiny)为核心的闸板阀开度检测方法。首先,将图像输入到检测网络并使用卷积进行特征提取;其次,为了提高检测模型对多尺度插板的检测精度,从增加网络感受野的角度出发,设计了一类将改进的空间金字塔池化(Spatial Pyramid Pooling,SPP)模块、Sub-stage特征融合和YOLO-tiny相结合的检测器SSA-YOLO(SPP and Sub-stage Aggregated YOLO),对插板及其边框的类别和位置信息进行端到端的预测;最后,检测器输出插板和闸板外框的类别及坐标,并利用它们的位置关系确定闸板阀的开度值。为了更加准确地衡量模型同时检测出插板及其闸板外框的能力,提出使用pairedAP(paired Average Precision)指标对检测模型进行评估。使用3种闸板阀在不同时段的3000张图像和相关监测视频作为数据集对所提方法进行试验,结果表明:2种SSA-YOLO模型在保证实时检测的基础上,其pairedAP指标比对应的YOLO-tiny基准模型分别提高了10.6%和36.2%,并增强了模型的抗干扰能力与泛化性能,即使在闸板开度值连续变化的情形下仍有效。笔者提出的闸板阀开度检测思路能扩展应用于可利用多目标物体之间的空间位置关系来确定特定检测量的问题中。展开更多
In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm...In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.展开更多
文摘闸板阀是煤矿生产过程中控制水、煤流量的常用设备。由于成本、布线等原因,非关键闸板阀的开度检测并未纳入到矿井集中监控系统中。鉴于此,在已有的视频监控系统基础上,针对现有基于图像处理的闸板阀开度检测算法存在需要多模型训练、多步检测、容错率低的问题,提出了一类以改进的YOLO-tiny(包括YOLOv3-tiny和YOLOv4-tiny)为核心的闸板阀开度检测方法。首先,将图像输入到检测网络并使用卷积进行特征提取;其次,为了提高检测模型对多尺度插板的检测精度,从增加网络感受野的角度出发,设计了一类将改进的空间金字塔池化(Spatial Pyramid Pooling,SPP)模块、Sub-stage特征融合和YOLO-tiny相结合的检测器SSA-YOLO(SPP and Sub-stage Aggregated YOLO),对插板及其边框的类别和位置信息进行端到端的预测;最后,检测器输出插板和闸板外框的类别及坐标,并利用它们的位置关系确定闸板阀的开度值。为了更加准确地衡量模型同时检测出插板及其闸板外框的能力,提出使用pairedAP(paired Average Precision)指标对检测模型进行评估。使用3种闸板阀在不同时段的3000张图像和相关监测视频作为数据集对所提方法进行试验,结果表明:2种SSA-YOLO模型在保证实时检测的基础上,其pairedAP指标比对应的YOLO-tiny基准模型分别提高了10.6%和36.2%,并增强了模型的抗干扰能力与泛化性能,即使在闸板开度值连续变化的情形下仍有效。笔者提出的闸板阀开度检测思路能扩展应用于可利用多目标物体之间的空间位置关系来确定特定检测量的问题中。
文摘In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.
