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基于图像过曝光区域校正的工业机器人镁锭堆垛分割算法

Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area.

作者信息

Li Qiguang, Zheng Huazheng, Wang Wensheng, Li Chenggang

机构信息

School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China.

Jiaxing Worldia Diamond Tools Co., Ltd., Jiaxing 314031, China.

出版信息

Sensors (Basel). 2023 Jul 30;23(15):6809. doi: 10.3390/s23156809.

DOI:10.3390/s23156809
PMID:37571592
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422230/
Abstract

This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%.

摘要

本文提出了一种基于图像过曝光区域校正的镁锭堆自适应阈值分割算法（ATSIOAC），该算法解决了外部环境光和辅助光源导致镁合金锭表面出现镜面反射的问题。首先，考虑映射图像的亮度和色度信息，将曝光概率阈值划分为弱曝光区域和强曝光区域。其次，利用镁锭区域与背景区域的饱和度差异获取镁锭区域的掩膜，以消除图像背景的干扰。然后，将过曝光区域中相邻像素的RGB均值作为参考，分别对强曝光区域和弱曝光区域的颜色进行校正。此外，为了平滑融合这两幅校正后的图像，采用了像素加权平均（WA）。最后，构建了镁锭分拣实验装置，并通过ATSIOAC对校正后的锭堆顶面图像进行分割。实验结果表明，本文提出的过曝光区域检测与校正算法能够有效校正过曝光区域的颜色信息，在分割锭图像时，可以获得锭堆顶面的完整分割结果有效地提高了镁合金锭分割的准确性。该分割算法的分割准确率达到了94.38%。

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