A Method for Detecting Depression Depth in UWF Images Based on Lightweight Unet Architecture
Depression depth detection is a key aspect of UWF image processing,but there are shortcomings in the current detection of depression depth in UWF images.In practice,the detection error is relatively large,and the confidence level of the detection results is low,which cannot achieve the expected detection effect.Therefore,this article proposes a depression depth detection method for UWF images based on a lightweight Unet architecture.The anisotropic filtering method is used to smooth the UWF image,and the lightweight Unet architecture is used for semantic segmentation of the UWF image.The concave regions of the UWF image are extracted,and the concave depth of the UWF image is calculated based on the linear relationship between image grayscale and depth,achieving concave depth detection of UWF images based on the lightweight Unet architecture.Experimental results have shown that the method proposed in this paper effectively reduces the detection error of indentation depth in UWF images and significantly improves the confidence level.The lightweight Unet architecture has good application prospects in the field of indentation depth detection in UWF images.
lightweight Unet architectureUWF imagedepth of depressiondetectionanisotropic filtering methodsemantic segmentation
万方数据
维普
