Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required....Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not;and from semantic perspective, MSE and PSNR are giving only absolute error;on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.展开更多
输电线路上的绝缘子长期处于强电场和恶劣环境中,其安全性的监测尤为重要。为了快速精确识别航拍图像中的绝缘子,提出了一种基于Gaussian YOLOv3(You only look once)的绝缘子检测算法。首先,通过增加网络的输出和改进网络的损失函数输...输电线路上的绝缘子长期处于强电场和恶劣环境中,其安全性的监测尤为重要。为了快速精确识别航拍图像中的绝缘子,提出了一种基于Gaussian YOLOv3(You only look once)的绝缘子检测算法。首先,通过增加网络的输出和改进网络的损失函数输出预测框。然后,结合高斯分布的策略输出对应预测框坐标的均值和方差。最后,采用多阶段迁移学习解决小数据集容易发生过拟合的问题。实验结果表明,本算法能准确定位物体的位置,在测试集中的绝缘子检测精度达到93.8%,绝缘子缺陷检测精度达到94.5%,优于同等条件下的Faster区域卷积神经网络和YOLOv3算法,为输电线路的绝缘子智能化检测提供了一定的参考价值。展开更多
文摘Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not;and from semantic perspective, MSE and PSNR are giving only absolute error;on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.
文摘输电线路上的绝缘子长期处于强电场和恶劣环境中,其安全性的监测尤为重要。为了快速精确识别航拍图像中的绝缘子,提出了一种基于Gaussian YOLOv3(You only look once)的绝缘子检测算法。首先,通过增加网络的输出和改进网络的损失函数输出预测框。然后,结合高斯分布的策略输出对应预测框坐标的均值和方差。最后,采用多阶段迁移学习解决小数据集容易发生过拟合的问题。实验结果表明,本算法能准确定位物体的位置,在测试集中的绝缘子检测精度达到93.8%,绝缘子缺陷检测精度达到94.5%,优于同等条件下的Faster区域卷积神经网络和YOLOv3算法,为输电线路的绝缘子智能化检测提供了一定的参考价值。
