Introduction of the photothermal effect into transition-metal oxide photoanodes has been proven to be an effective method to improve the photoelectrochemical(PEC)water-splitting performance.However,the precise role of...Introduction of the photothermal effect into transition-metal oxide photoanodes has been proven to be an effective method to improve the photoelectrochemical(PEC)water-splitting performance.However,the precise role of the photothermal effect on the PEC performance of photoanodes is still not well understood.Herein,spinel-structured ZnFe_(2)O_(4)nanoparticles are deposited on the surface of hematite(Fe_(2)O_(3)),and the ZnFe_(2)O_(4)/Fe_(2)O_(3)photoanode achieves a high photocurrent density of 3.17 mA cm^(-2)at 1.23 V versus a reversible hydrogen electrode(VRHE)due to the photothermal effect of ZnFe_(2)O_(4).Considering that the hopping of electron small polarons induced by oxygen vacancies is thermally activated,we clarify that the main reason for the enhanced PEC performance via the photothermal effect is the promoted mobility of electron small polarons that are bound to positively charged oxygen vacancies.Under the synergistic effect of oxygen vacancies and the photothermal effect,the electron conductivity and PEC performance are significantly improved,which provide fundamental insights into the impact of the photothermal effect on the PEC performance of small polaron-type semiconductor photoanodes.展开更多
无人机高空航拍图像中车辆像素占比极低,目标可视化信息较少,在目标检测任务中容易漏检和误检。因此,本文提出一种基于改进YOLOX(You Only Look Once X)的无人机高空航拍视角下小尺度车辆精确检测方法。首先,为增强网络对低级特征的提...无人机高空航拍图像中车辆像素占比极低,目标可视化信息较少,在目标检测任务中容易漏检和误检。因此,本文提出一种基于改进YOLOX(You Only Look Once X)的无人机高空航拍视角下小尺度车辆精确检测方法。首先,为增强网络对低级特征的提取能力,在原始YOLOX预测头部增加一个160 pixel×160 pixel的浅层特征提取网络;其次,在骨干网络后端嵌入基于归一化的注意力机制模块(Normalization-based Attention Module,NAM),以抑制冗余的非显著特征表达;最后,为了增大小尺度车辆的相对像素比,提升网络捕捉有效特征信息的能力,提出一种基于滑动窗口的图像切分检测方法。试验结果表明,改进YOLOX网络表现出良好的检测效能,检测精度达到了84.58%,优于典型的目标检测网络Faster R-CNN(79.95%)、YOLOv3(83.69%)、YOLOv5(84.31%)及YOLOX(83.10%)。此外,改进YOLOX能够有效解决无人机高空航拍图像中小尺度车辆的漏检和误检问题,且预测框更贴合车辆的实际轮廓;同时,在不同航拍高度的目标检测任务中具有较高的鲁棒性。展开更多
基金This work was supported by the National Natural Science Foundation of China(51902297,52002361,52003300,and 22109120)the Zhejiang Provincial Natural Science Foundation of China(LQ21B030002)the fund of the Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education,and Hubei Key Laboratory of Catalysis and Materials Science.
文摘Introduction of the photothermal effect into transition-metal oxide photoanodes has been proven to be an effective method to improve the photoelectrochemical(PEC)water-splitting performance.However,the precise role of the photothermal effect on the PEC performance of photoanodes is still not well understood.Herein,spinel-structured ZnFe_(2)O_(4)nanoparticles are deposited on the surface of hematite(Fe_(2)O_(3)),and the ZnFe_(2)O_(4)/Fe_(2)O_(3)photoanode achieves a high photocurrent density of 3.17 mA cm^(-2)at 1.23 V versus a reversible hydrogen electrode(VRHE)due to the photothermal effect of ZnFe_(2)O_(4).Considering that the hopping of electron small polarons induced by oxygen vacancies is thermally activated,we clarify that the main reason for the enhanced PEC performance via the photothermal effect is the promoted mobility of electron small polarons that are bound to positively charged oxygen vacancies.Under the synergistic effect of oxygen vacancies and the photothermal effect,the electron conductivity and PEC performance are significantly improved,which provide fundamental insights into the impact of the photothermal effect on the PEC performance of small polaron-type semiconductor photoanodes.
文摘无人机高空航拍图像中车辆像素占比极低,目标可视化信息较少,在目标检测任务中容易漏检和误检。因此,本文提出一种基于改进YOLOX(You Only Look Once X)的无人机高空航拍视角下小尺度车辆精确检测方法。首先,为增强网络对低级特征的提取能力,在原始YOLOX预测头部增加一个160 pixel×160 pixel的浅层特征提取网络;其次,在骨干网络后端嵌入基于归一化的注意力机制模块(Normalization-based Attention Module,NAM),以抑制冗余的非显著特征表达;最后,为了增大小尺度车辆的相对像素比,提升网络捕捉有效特征信息的能力,提出一种基于滑动窗口的图像切分检测方法。试验结果表明,改进YOLOX网络表现出良好的检测效能,检测精度达到了84.58%,优于典型的目标检测网络Faster R-CNN(79.95%)、YOLOv3(83.69%)、YOLOv5(84.31%)及YOLOX(83.10%)。此外,改进YOLOX能够有效解决无人机高空航拍图像中小尺度车辆的漏检和误检问题,且预测框更贴合车辆的实际轮廓;同时,在不同航拍高度的目标检测任务中具有较高的鲁棒性。
