The Supervised CNN Image Edge Detection Algorithm in Scotopic Vision Environment

Qin Zhang, Xiangling Zhou, Xiaowen Xu, Xinhong Xie, Mengyuan Zhang, Yuxiang Tao, Ke Li, Zhiqiang Zhao
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引用次数: 2

Abstract

Scotopic vision environment images are characterized by low contrast and some details hidden in the image background, that causes human eyes are hard to detect and brings difficulties to the subsequent application of computer vision tasks. In order to solve the problem that many false edges are generated when DexiNed (Dense Extreme Inception Network for Edge Detection) model detects scotopic vision images, an improved DexiNed edge detection model was proposed in this paper. The improved edge detection model retained the backbone network of the DexiNed model. By adding the convolution layers and residual units in the appropriate position of the DexiNed model, the model can eliminate most of the false edges generated by the DexiNed model in the scotopic vision images better. In order to further improve the edge detection accuracy of scotopic vision image by the improved DexiNed model, this paper builds scotopic vision image training set based on edge annotation data set BIPED (Barcelona Images for Perceptual Edge Detection) from RGB and YUV color space respectively. And scotopic vision image test dataset results showed that the effect of scotopic vision image based on RGB color space to have better performance, because edge continuity was better and the edge detection model of MSE (mean square error) index dropped, PSNR (peak signal to noise ratio) and SSIM (structural similarity) index raised.
暗域视觉环境下有监督CNN图像边缘检测算法
暗域视觉环境图像的特点是对比度低,一些细节隐藏在图像背景中,人眼很难检测到,给计算机视觉任务的后续应用带来了困难。针对dexine (Dense Extreme Inception Network for Edge Detection)模型检测暗位视觉图像时产生大量假边的问题,提出了一种改进的dexine边缘检测模型。改进的边缘检测模型保留了dexine模型的骨干网络。通过在dexine模型的适当位置添加卷积层和残差单元,该模型可以较好地消除暗域视觉图像中dexine模型产生的大部分假边缘。为了利用改进的dexine模型进一步提高暗域视觉图像的边缘检测精度,本文分别基于RGB和YUV颜色空间的边缘标注数据集BIPED (Barcelona Images for Perceptual edge detection)构建了暗域视觉图像训练集。而暗域视觉图像测试数据集的结果表明,基于RGB色彩空间的暗域视觉图像具有更好的效果,因为边缘连续性更好,边缘检测模型的MSE(均方误差)指数下降,PSNR(峰值信噪比)和SSIM(结构相似度)指数提高。
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