Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and diffe...Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore sati展开更多
文摘Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore sati
文摘为了解氯吡苯脲(1-(2-chloropyridin-4-yl)-3-phenylurea,CPPU)对生长期‘徐香’猕猴桃光学参数和内部品质的影响以及光学参数与内部品质的关系,采用单积分球系统(950~1650 nm)测定经不同质量浓度(0、10、20 mg/L)CPPU处理的生长期猕猴桃的光学吸收系数(μ_(a))和约化散射系数(μ_(s)’),并测定猕猴桃的内部品质(可溶性固形物含量(soluble solids content,SSC)、含水率及硬度);分析光学参数与内部品质之间的关系,并建立预测内部品质的偏最小二乘回归(partial least squares regression,PLSR)模型。结果表明,CPPU处理使得猕猴桃的硬度降低,含水率升高,但对SSC无显著影响(P>0.05),且CPPU处理导致猕猴桃的光学参数值发生变化;μ_(a)和μ_(s)’与猕猴桃同一种内部品质之间呈现不同的正负相关性,且相关系数随波长而变化,并在某一波段内有较好的相关性;基于μ_(a)谱建立的PLSR模型对猕猴桃SSC和含水率的预测效果最优(预测集相关系数(R_(p))=0.709,预测集均方根误差(root mean squares error of prediction,RMSEP)=0.579%;R_(p)=0.790,RMSEP=0.408%),而基于μ_(a)+μ_(s)’谱所建模型对硬度的预测性能最佳(R_(p)=0.796,RMSEP=7.890 N)。该研究可为基于光谱技术预测猕猴桃内部品质提供理论依据。
