This study focuses on developing deep learning methods for small and dim target detection.We model infrared images as the union of the target region and background region.Based on this model,the target detection probl...This study focuses on developing deep learning methods for small and dim target detection.We model infrared images as the union of the target region and background region.Based on this model,the target detection problem is considered a two‐class segmentation problem that divides an image into the target and background.Therefore,a neural network called SDDNet for single‐frame images is constructed.The network yields target extraction results according to the original images.For multiframe images,a network called IC‐SDDNet,a combination of SDDNet and an interframe correlation network module is constructed.SDDNet and IC‐SDDNet achieve target detection rates close to 1 on typical datasets with very low false positives,thereby performing significantly better than current methods.Both models can be executed end to end,so both are very convenient to use,and their implementation efficiency is very high.Average speeds of 540+/230+FPS and 170+/60+FPS are achieved with SDDNet and IC‐SDDNet on a single Tesla V100 graphics processing unit and a single Jetson TX2 embedded module respectively.Additionally,neither network needs to use future information,so both networks can be directly used in real‐time systems.The well‐trained models and codes used in this study are available at https://github.com/LittlePieces/ObjectDetection.展开更多
采用快速液相烧结法制备Bi_(1-x)Pr_(x)Fe_(1-x)Ti_(x)O_(3)(x=0.00、0.03、0.06、0.12)系列多铁陶瓷样品,研究Pr-Ti共掺杂对BiFe O_(3)结构、缺陷、电学和磁学特性的影响。XRD分析结果表明:所有样品均为菱方钙钛矿结构,Pr-Ti共掺杂可...采用快速液相烧结法制备Bi_(1-x)Pr_(x)Fe_(1-x)Ti_(x)O_(3)(x=0.00、0.03、0.06、0.12)系列多铁陶瓷样品,研究Pr-Ti共掺杂对BiFe O_(3)结构、缺陷、电学和磁学特性的影响。XRD分析结果表明:所有样品均为菱方钙钛矿结构,Pr-Ti共掺杂可有效抑制杂相生成,当掺杂量高于0.06时杂相基本消失,共掺杂引起结构畸变。正电子湮没寿命谱测试结果表明:所有样品中均存在阳离子空位型缺陷,空位尺寸和浓度均随Pr-Ti掺杂量增加而增大。电学和磁学性能测试结果表明:适量Pr-Ti共掺杂可有效提高Bi Fe O_(3)的介电、铁电和磁学性能。综合上述结果,认为BiFeO_(3)多铁性能的改善可能是由于Pr-Ti共掺杂引起晶格畸变、减少氧空位浓度、改变阳离子空位浓度等多种原因引起。展开更多
文摘This study focuses on developing deep learning methods for small and dim target detection.We model infrared images as the union of the target region and background region.Based on this model,the target detection problem is considered a two‐class segmentation problem that divides an image into the target and background.Therefore,a neural network called SDDNet for single‐frame images is constructed.The network yields target extraction results according to the original images.For multiframe images,a network called IC‐SDDNet,a combination of SDDNet and an interframe correlation network module is constructed.SDDNet and IC‐SDDNet achieve target detection rates close to 1 on typical datasets with very low false positives,thereby performing significantly better than current methods.Both models can be executed end to end,so both are very convenient to use,and their implementation efficiency is very high.Average speeds of 540+/230+FPS and 170+/60+FPS are achieved with SDDNet and IC‐SDDNet on a single Tesla V100 graphics processing unit and a single Jetson TX2 embedded module respectively.Additionally,neither network needs to use future information,so both networks can be directly used in real‐time systems.The well‐trained models and codes used in this study are available at https://github.com/LittlePieces/ObjectDetection.
文摘采用快速液相烧结法制备Bi_(1-x)Pr_(x)Fe_(1-x)Ti_(x)O_(3)(x=0.00、0.03、0.06、0.12)系列多铁陶瓷样品,研究Pr-Ti共掺杂对BiFe O_(3)结构、缺陷、电学和磁学特性的影响。XRD分析结果表明:所有样品均为菱方钙钛矿结构,Pr-Ti共掺杂可有效抑制杂相生成,当掺杂量高于0.06时杂相基本消失,共掺杂引起结构畸变。正电子湮没寿命谱测试结果表明:所有样品中均存在阳离子空位型缺陷,空位尺寸和浓度均随Pr-Ti掺杂量增加而增大。电学和磁学性能测试结果表明:适量Pr-Ti共掺杂可有效提高Bi Fe O_(3)的介电、铁电和磁学性能。综合上述结果,认为BiFeO_(3)多铁性能的改善可能是由于Pr-Ti共掺杂引起晶格畸变、减少氧空位浓度、改变阳离子空位浓度等多种原因引起。
