为了在光学实验中合理应用数字成像设备,需要获取并处理其物理输入量与数字输出量之间的关系。基于ISO 14524:2009标准,对数码单反相机Canon EOS 500D的焦平面光电转换函数进行了实验测量及数据分析。通过比较不同尺寸像素区域及不同图...为了在光学实验中合理应用数字成像设备,需要获取并处理其物理输入量与数字输出量之间的关系。基于ISO 14524:2009标准,对数码单反相机Canon EOS 500D的焦平面光电转换函数进行了实验测量及数据分析。通过比较不同尺寸像素区域及不同图片格式对测量结果的影响,发现焦平面光电转换函数的测量偏差随像素区域尺寸的减小而增大,而RAW和JPEG两种文件格式的数字输出值与焦平面曝光量的关系分别为线性和非线性。当单反相机用作光分布记录测量的仪器时,采用直接反应传感器接收的辐照度信息的RAW格式作为输出数据,可以获得较大的动态范围,理论上可以避免相机非线性变换等后续处理带来的影响,经过必要的校正可以更精确地表示场景信息。展开更多
In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera an...In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN.Thereafter,training,validation,and testing of the DCNNs were performed based on the DSLR camera and microscope image data.Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy.The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera,which was beneficial for extracting a larger number of features.Moreover,the DSLR camera procured more realistic images than the microscope.Thus,when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera,time and cost were reduced,whereas the usefulness increased.Furthermore,an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions.In addition,it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures,such as salt damage,carbonation,sulfation,corrosion,and freezing-thawing.展开更多
文摘为了在光学实验中合理应用数字成像设备,需要获取并处理其物理输入量与数字输出量之间的关系。基于ISO 14524:2009标准,对数码单反相机Canon EOS 500D的焦平面光电转换函数进行了实验测量及数据分析。通过比较不同尺寸像素区域及不同图片格式对测量结果的影响,发现焦平面光电转换函数的测量偏差随像素区域尺寸的减小而增大,而RAW和JPEG两种文件格式的数字输出值与焦平面曝光量的关系分别为线性和非线性。当单反相机用作光分布记录测量的仪器时,采用直接反应传感器接收的辐照度信息的RAW格式作为输出数据,可以获得较大的动态范围,理论上可以避免相机非线性变换等后续处理带来的影响,经过必要的校正可以更精确地表示场景信息。
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2018R1A2B6007333)This study was supported by 2018 Research Grant from Kangwon National University.
文摘In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN.Thereafter,training,validation,and testing of the DCNNs were performed based on the DSLR camera and microscope image data.Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy.The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera,which was beneficial for extracting a larger number of features.Moreover,the DSLR camera procured more realistic images than the microscope.Thus,when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera,time and cost were reduced,whereas the usefulness increased.Furthermore,an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions.In addition,it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures,such as salt damage,carbonation,sulfation,corrosion,and freezing-thawing.
