针对目前Anchor-free目标检测方法CenterNet(Objects as Points)生成热力图不准确、检测精度不足的问题,提出了一种基于特征迭代聚合的高分辨率表征网络CenterNet-DHRNet。首先,引入高分辨率表征骨干网络,并用迭代聚合的方式对不同分辨...针对目前Anchor-free目标检测方法CenterNet(Objects as Points)生成热力图不准确、检测精度不足的问题,提出了一种基于特征迭代聚合的高分辨率表征网络CenterNet-DHRNet。首先,引入高分辨率表征骨干网络,并用迭代聚合的方式对不同分辨率的特征图进行融合,提高网络的分辨率,有效减少图像在下采样过程中损失的空间语义信息。其次,使用高效通道注意力机制对高分辨率表征骨干网络的输出进行优化。最后,利用结合空洞卷积的空间金字塔池化操作增强网络对不同尺度物体的感受野。实验在PASCAL VOC数据集和KITTI数据集上进行,结果表明:CenterNet-DHRNet精度更高,满足实时检测的性能要求,具有良好的鲁棒性。展开更多
Geographic location of nodes is very useful in a sensor network. Previous localization algorithms assume that there exist some anchor nodes in this kind of network, and then other nodes are estimated to create their c...Geographic location of nodes is very useful in a sensor network. Previous localization algorithms assume that there exist some anchor nodes in this kind of network, and then other nodes are estimated to create their coordinates. Once there are not anchors to be deployed, those localization algorithms will be invalidated. Many papers in this field focus on anchor-based solutions. The use of anchors introduces many limitations, since anchors require external equipments such as global position system, cause additional power consumption. A novel positioning algorithm is proposed to use a virtual coordinate system based on a new concept--virtual anchor. It is executed in a distributed fashion according to the connectivity of a node and the measured distances to its neighbors. Both the adjacent member information and the ranging distance result are combined to generate the estimated position of a network, one of which is independently adopted for localization previously. At the position refinement stage the intermediate estimation of a node begins to be evaluated on its reliability for position mutation; thus the positioning optimization process of the whole network is avoided falling into a local optimal solution. Simulation results prove that the algorithm can resolve the distributed localization problem for anchor-free sensor networks, and is superior to previous methods in terms of its positioning capability under a variety of circumstances.展开更多
由于Anchor-based方法存的一些问题,如目标不规则、手动设计anchor、匹配机制无法匹配极端目标等,提出使用Anchor-free方法用于安防领域的行人与车辆的检测。论文在利用CornerNet-lite进行目标检测的基础上,提出“同类别匹配抑制规则”...由于Anchor-based方法存的一些问题,如目标不规则、手动设计anchor、匹配机制无法匹配极端目标等,提出使用Anchor-free方法用于安防领域的行人与车辆的检测。论文在利用CornerNet-lite进行目标检测的基础上,提出“同类别匹配抑制规则”算法,以降低误报率。所提出的算法基于真实场景的行人与车辆数据集进行测试评估,在不同的场景下,如烈日、阴天、雨雪、夜晚、白天等。实验结果表明,使用安防数据集测试时,改进的算法平均精度(Mean Average Precision,mAP)为0.35,比原方案提高0.05。论文所提出的算法为一般的目标检测算法提供高检测率和低误报率。该算法是有效的,为开发实时行人与车辆检测算法铺平了道路。展开更多
文摘针对目前Anchor-free目标检测方法CenterNet(Objects as Points)生成热力图不准确、检测精度不足的问题,提出了一种基于特征迭代聚合的高分辨率表征网络CenterNet-DHRNet。首先,引入高分辨率表征骨干网络,并用迭代聚合的方式对不同分辨率的特征图进行融合,提高网络的分辨率,有效减少图像在下采样过程中损失的空间语义信息。其次,使用高效通道注意力机制对高分辨率表征骨干网络的输出进行优化。最后,利用结合空洞卷积的空间金字塔池化操作增强网络对不同尺度物体的感受野。实验在PASCAL VOC数据集和KITTI数据集上进行,结果表明:CenterNet-DHRNet精度更高,满足实时检测的性能要求,具有良好的鲁棒性。
基金the National Natural Science Foundation of China (60673054, 60773129)theExcellent Youth Science and Technology Foundation of Anhui Province of China.
文摘Geographic location of nodes is very useful in a sensor network. Previous localization algorithms assume that there exist some anchor nodes in this kind of network, and then other nodes are estimated to create their coordinates. Once there are not anchors to be deployed, those localization algorithms will be invalidated. Many papers in this field focus on anchor-based solutions. The use of anchors introduces many limitations, since anchors require external equipments such as global position system, cause additional power consumption. A novel positioning algorithm is proposed to use a virtual coordinate system based on a new concept--virtual anchor. It is executed in a distributed fashion according to the connectivity of a node and the measured distances to its neighbors. Both the adjacent member information and the ranging distance result are combined to generate the estimated position of a network, one of which is independently adopted for localization previously. At the position refinement stage the intermediate estimation of a node begins to be evaluated on its reliability for position mutation; thus the positioning optimization process of the whole network is avoided falling into a local optimal solution. Simulation results prove that the algorithm can resolve the distributed localization problem for anchor-free sensor networks, and is superior to previous methods in terms of its positioning capability under a variety of circumstances.
文摘由于Anchor-based方法存的一些问题,如目标不规则、手动设计anchor、匹配机制无法匹配极端目标等,提出使用Anchor-free方法用于安防领域的行人与车辆的检测。论文在利用CornerNet-lite进行目标检测的基础上,提出“同类别匹配抑制规则”算法,以降低误报率。所提出的算法基于真实场景的行人与车辆数据集进行测试评估,在不同的场景下,如烈日、阴天、雨雪、夜晚、白天等。实验结果表明,使用安防数据集测试时,改进的算法平均精度(Mean Average Precision,mAP)为0.35,比原方案提高0.05。论文所提出的算法为一般的目标检测算法提供高检测率和低误报率。该算法是有效的,为开发实时行人与车辆检测算法铺平了道路。
