{"title":"融合宏观和微观特征的砾石自动筛分法","authors":"","doi":"10.1016/j.ijsrc.2024.05.002","DOIUrl":null,"url":null,"abstract":"<div><p>Measuring the grain size distribution (GSD) of unconsolidated particles is critical to understanding coastal spreading, riverbed dynamics, and sediment transport. The current study presents a novel gravel automatic sieving (GAS) method designed to improve the accuracy and reliability of particle size analyses. At the macroscopic, the method utilizes the convex hull property of gravel to define the maximum extent of the searched gravel, effectively reducing over and under-segmentation problems. At the microscopic, the accuracy of gravel segmentation is improved by analyzing the color space characteristics of gravel to identify the pixel patches of gravel accurately. To validate the effectiveness of the GAS method, the proposed method was tested in both the laboratory and the field. In the laboratory, four artificial samples were processed using the GAS method, and the results were compared with those obtained using the traditional sieving method. The results showed that the correlation coefficients between the GAS method and the traditional sieving method ranged from 94.3% to 97.8%, and the relative errors ranged from 5.8% to 20.9%, demonstrating the validity of the GAS method. In addition, the application of ImageJ software to manually identify the particle size method (ImageJ method) was also compared with the mechanical sieving method, and the correlation coefficient between the two methods was greater than 98.2%, and the relative error was less than 10.9%, so the ImageJ method can be used as a standardized method to measure the other methods. In the field, sixteen images taken in four different regions and at different times were analyzed using the ImageJ method as a benchmark. The performance of the automatic with image filtering (AIF), BASEGRAIN, and the GAS methods also were compared. The results show that the relative errors range from 28.1% to 94.6% for the BASEGRAIN, 16.8% to 1003.6% for the AIF method, and only 5.6% to 30.7% for the GAS method. As a result, the GAS method demonstrates higher accuracy and stability in complex environments.</p></div>","PeriodicalId":50290,"journal":{"name":"International Journal of Sediment Research","volume":"39 4","pages":"Pages 601-614"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1001627924000556/pdfft?md5=8a1cac5b1f7c12df2a30552f8cb4f597&pid=1-s2.0-S1001627924000556-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Gravel automatic sieving method fusing macroscopic and microscopic characteristics\",\"authors\":\"\",\"doi\":\"10.1016/j.ijsrc.2024.05.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Measuring the grain size distribution (GSD) of unconsolidated particles is critical to understanding coastal spreading, riverbed dynamics, and sediment transport. The current study presents a novel gravel automatic sieving (GAS) method designed to improve the accuracy and reliability of particle size analyses. At the macroscopic, the method utilizes the convex hull property of gravel to define the maximum extent of the searched gravel, effectively reducing over and under-segmentation problems. At the microscopic, the accuracy of gravel segmentation is improved by analyzing the color space characteristics of gravel to identify the pixel patches of gravel accurately. To validate the effectiveness of the GAS method, the proposed method was tested in both the laboratory and the field. In the laboratory, four artificial samples were processed using the GAS method, and the results were compared with those obtained using the traditional sieving method. The results showed that the correlation coefficients between the GAS method and the traditional sieving method ranged from 94.3% to 97.8%, and the relative errors ranged from 5.8% to 20.9%, demonstrating the validity of the GAS method. In addition, the application of ImageJ software to manually identify the particle size method (ImageJ method) was also compared with the mechanical sieving method, and the correlation coefficient between the two methods was greater than 98.2%, and the relative error was less than 10.9%, so the ImageJ method can be used as a standardized method to measure the other methods. In the field, sixteen images taken in four different regions and at different times were analyzed using the ImageJ method as a benchmark. The performance of the automatic with image filtering (AIF), BASEGRAIN, and the GAS methods also were compared. The results show that the relative errors range from 28.1% to 94.6% for the BASEGRAIN, 16.8% to 1003.6% for the AIF method, and only 5.6% to 30.7% for the GAS method. 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引用次数: 0
摘要
测量未固结颗粒的粒度分布(GSD)对于了解海岸扩张、河床动力学和沉积物输运至关重 要。本研究提出了一种新颖的砾石自动筛分(GAS)方法,旨在提高粒度分析的准确性和可靠性。在宏观上,该方法利用砾石的凸壳特性来确定搜索砾石的最大范围,从而有效地减少了过筛和欠筛问题。在微观上,通过分析砾石的色彩空间特征,准确识别出砾石的像素斑块,从而提高了砾石分割的准确性。为了验证 GAS 方法的有效性,对所提出的方法进行了实验室和实地测试。在实验室中,使用 GAS 方法处理了四个人工样本,并将结果与使用传统筛分方法得到的结果进行了比较。结果表明,GAS 法与传统筛分法的相关系数在 94.3% 至 97.8% 之间,相对误差在 5.8% 至 20.9% 之间,证明了 GAS 法的有效性。此外,应用 ImageJ 软件手动识别粒度的方法(ImageJ 方法)也与机械筛分法进行了比较,两种方法的相关系数大于 98.2%,相对误差小于 10.9%,因此 ImageJ 方法可作为衡量其他方法的标准化方法。在现场,以 ImageJ 方法为基准,分析了在四个不同区域和不同时间拍摄的 16 幅图像。此外,还比较了自动图像滤波 (AIF)、BASEGRAIN 和 GAS 方法的性能。结果显示,BASEGRAIN 方法的相对误差在 28.1%至 94.6%之间,AIF 方法的相对误差在 16.8%至 1003.6%之间,而 GAS 方法的相对误差仅在 5.6%至 30.7%之间。因此,GAS 方法在复杂环境中表现出更高的准确性和稳定性。
Gravel automatic sieving method fusing macroscopic and microscopic characteristics
Measuring the grain size distribution (GSD) of unconsolidated particles is critical to understanding coastal spreading, riverbed dynamics, and sediment transport. The current study presents a novel gravel automatic sieving (GAS) method designed to improve the accuracy and reliability of particle size analyses. At the macroscopic, the method utilizes the convex hull property of gravel to define the maximum extent of the searched gravel, effectively reducing over and under-segmentation problems. At the microscopic, the accuracy of gravel segmentation is improved by analyzing the color space characteristics of gravel to identify the pixel patches of gravel accurately. To validate the effectiveness of the GAS method, the proposed method was tested in both the laboratory and the field. In the laboratory, four artificial samples were processed using the GAS method, and the results were compared with those obtained using the traditional sieving method. The results showed that the correlation coefficients between the GAS method and the traditional sieving method ranged from 94.3% to 97.8%, and the relative errors ranged from 5.8% to 20.9%, demonstrating the validity of the GAS method. In addition, the application of ImageJ software to manually identify the particle size method (ImageJ method) was also compared with the mechanical sieving method, and the correlation coefficient between the two methods was greater than 98.2%, and the relative error was less than 10.9%, so the ImageJ method can be used as a standardized method to measure the other methods. In the field, sixteen images taken in four different regions and at different times were analyzed using the ImageJ method as a benchmark. The performance of the automatic with image filtering (AIF), BASEGRAIN, and the GAS methods also were compared. The results show that the relative errors range from 28.1% to 94.6% for the BASEGRAIN, 16.8% to 1003.6% for the AIF method, and only 5.6% to 30.7% for the GAS method. As a result, the GAS method demonstrates higher accuracy and stability in complex environments.
期刊介绍:
International Journal of Sediment Research, the Official Journal of The International Research and Training Center on Erosion and Sedimentation and The World Association for Sedimentation and Erosion Research, publishes scientific and technical papers on all aspects of erosion and sedimentation interpreted in its widest sense.
The subject matter is to include not only the mechanics of sediment transport and fluvial processes, but also what is related to geography, geomorphology, soil erosion, watershed management, sedimentology, environmental and ecological impacts of sedimentation, social and economical effects of sedimentation and its assessment, etc. Special attention is paid to engineering problems related to sedimentation and erosion.