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Pedestrian attribute recognition is often considered as a multi-label image classification task. In order to make full use of attribute-related location information, a saliency guided sel-attention network ( SGSA-Net) was proposed to weakly supervise attribute localization, without annotations of attribute-related regions. Saliency priors were integrated into the spatial attention module ( SAM ). Meanwhile,channel-wise attention and spatial attention were introduced into the network. Moreover, a weighted binary cross-entropy loss ( WCEL) function was employed to handle the imbalance of training data. Extensive experiments on richly annotated pedestrian ( RAP) and pedestrian attribute ( PETA) datasets demonstrated that SGSA-Net outperformed other state-of-the-art methods. 相似文献
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为实现自然水细菌总数快速检测的需求,基于三磷酸腺苷(ATP)生物发光法,研制了K2010型水质细菌总数快速检测仪。采用研制的快速检测仪分别对大肠杆菌标准品和太湖水实际样品进行了检测。此检测仪集成高精度AD转换器件,以小型光电倍增管(PMT)为光电转换器件,通过合理的电路、光路设计,结合实验室的自制试剂,实现了对自然水环境中细菌总数的快速检测;并可以通过GPS模块进行定位,将位置信息和检测结果通过GPRS模块传到远程终端。对大肠杆菌标准品进行检测的结果表明,在4.6×101~4.67×107CFU/mL范围内,相关性达到0.951 5,检测下限达到46CFU/mL,满足水质细菌总数检测需求。针对自然水环境检测的需求,对3种不同的水处理方法进行了比较分析,在此基础上分别在夏季和冬季对太湖水进行了采样和检测。结果表明,夏季,采集到23个样本,与国标法对比发现,在102-105CFU/mL浓度范围内两者相关系数R=0.916 9;冬季采集到40个样本,水中细菌浓度较低(在101~104CFU/mL之间)。检测结果说明,研制的K2010检测仪能正确区分富营养水质和贫、中营养水质,且检测速度快,准确度较高,重复性良好,可在水环境监测中用于水质细菌的快速筛查。 相似文献
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