基于语义特征提取的砂岩薄片图像颗粒分割方法

The plant leaf veins coupling feature representation and measurement method based on DeepLabV3+ is proposed to solve problems of slow segmentation, partial occlusion of leaf veins, and low measurement accuracy of leaf veins parameters. Firstly, to solve the problem of slow segmentation, the lightweight MobileNetV2 is selected as the extraction network for DeepLabV3+. On this basis, the Convex Hull-Scan method is applied to repair leaf veins. Subsequently, a refinement algorithm, Floodfill MorphologyEx Medianblur Morphological Skeleton (F-3MS), is proposed, reducing the burr phenomenon of leaf veins’ skeleton lines. Finally, leaf veins’ related parameters are measured. In this study, mean intersection over union (MIoU) and mean pixel accuracy (mPA) reach 81.50% and 92.89%, respectively, and the average segmentation speed reaches 9.81 frames per second. Furthermore, the network model parameters are compressed by 89.375%, down to 5.813M. Meanwhile, leaf veins’ length and width are measured, yielding an accuracy of 96.3642% and 96.1358%, respectively.

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基于语义特征提取的砂岩薄片图像颗粒分割方法

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A Review of Artificial Intelligence Methods for Seed Quality Inspection based on Spectral Imaging and Analysis

A Review of Artificial Intelligence Methods for Seed Quality Inspection based on Spectral Imaging and Analysis

Plant leaf veins coupling feature representation and measurement method based on DeepLabV3+

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