Changes

On November 28, 2024 at 12:49:02 PM UTC, admin:

Set author of Deep Granulometry to Coenen, Max (previously Max Coenen)

Updated description of Deep Granulometry from
This repository contains the data related to the paper ** "Deep Granulometry: Image based estimation of concrete aggregate size distributions using deep learning" ** where a deep learning based method is proposed for the image based determination of concrete aggregate grading curves (cf. video). [![Watch the video](https://github.com/MaximilianCoenen/MaxCoenen.github.io/blob/master/resources/images/klicktoplay2.png?raw=true)](https://maximiliancoenen.github.io/MaxCoenen.github.io/resources/images/2023_DeepGranulometry.mp4) More specifically, the data set consists of images showing concrete aggregate particles and reference data of the particle size distribution (grading curves) associated to each image. It is distinguished between the **CoarseAggregateData** and the **FineAggregateData**.     # Coarse Aggregate Data The __coarse__ data consists of aggregate samples with different particles sizes ranging from 0.1 mm to 32 mm. The grading curves are designed by linearly interpolation between a very fine and a very coarse distribution for three variants with maximum grain sizes of 8 mm, 16 mm, and 32 mm, respectively. For each variant, we designed eleven grading curves, resulting in a total number 33, which are shown in the figure below. For each sample, we acquired 50 images with a GSD of 0.125 mm, resulting in a data set of 1650 images in total. Example images for a subset of the grading curves of this data set are shown in the following figure. ![Example images and grading curves of the coarse data set](https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/8cb30616-5b24-4028-9c1d-ea250ac8ac84/download/examplecoarse.png " ")     # Fine Aggregate Data Similar to the previous data set, the __fine__ data set contains grading curves for the fine fraction of concrete aggregate of 0 to 2 mm with a GSD of 28.5 $\mu$m. We defined two base distributions of different shapes for the upper and lower bound, respectively, resulting in two interpolated grading curve sets (Set A and Set B). In total, 1700 images of 34 different particle size distributions were acquired. Example images of the data set and the corresponding grading curves are shown in the figure below. ![Example images and grading curves of the finedata set](https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/c56f4298-9663-457f-aaa7-0ba113fec4c9/download/examplefine.png " ") ## Related publications: If you make use of the proposed data, please cite one of the publication listed below. * **Coenen, M., Beyer, D., and Haist, M., 2023**: Granulometry Transformer: Image-based Granulometry of Concrete Aggregate for an automated Concrete Production Control. In: Proceedings of the European Conference on Computing in Construction (EC3). * **Coenen, M., Beyer, D., Ponick, A., Heipke, C. and Haist, M., 2023**: Deep Granulometry: Image based estimation of concrete aggregate size distributions using deep learning. _To be published_. ## Source Code Source code for CNN based prediction of grading curves using the data set can be found on github under following [Link](https://github.com/MaximilianCoenen/DeepGranulometry.git).
to
This repository contains the data related to the paper ** "Granulometry transformer: image-based granulometry of concrete aggregate for an automated concrete production control" ** where a deep learning based method is proposed for the image based determination of concrete aggregate grading curves (cf. video). [![Watch the video](https://github.com/MaximilianCoenen/MaxCoenen.github.io/blob/master/resources/images/klicktoplay2.png?raw=true)](https://maximiliancoenen.github.io/MaxCoenen.github.io/resources/images/2023_DeepGranulometry.mp4) More specifically, the data set consists of images showing concrete aggregate particles and reference data of the particle size distribution (grading curves) associated to each image. It is distinguished between the **CoarseAggregateData** and the **FineAggregateData**.     # Coarse Aggregate Data The __coarse__ data consists of aggregate samples with different particles sizes ranging from 0.1 mm to 32 mm. The grading curves are designed by linearly interpolation between a very fine and a very coarse distribution for three variants with maximum grain sizes of 8 mm, 16 mm, and 32 mm, respectively. For each variant, we designed eleven grading curves, resulting in a total number 33, which are shown in the figure below. For each sample, we acquired 50 images with a GSD of 0.125 mm, resulting in a data set of 1650 images in total. Example images for a subset of the grading curves of this data set are shown in the following figure. ![Example images and grading curves of the coarse data set](https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/8cb30616-5b24-4028-9c1d-ea250ac8ac84/download/examplecoarse.png " ")     # Fine Aggregate Data Similar to the previous data set, the __fine__ data set contains grading curves for the fine fraction of concrete aggregate of 0 to 2 mm with a GSD of 28.5 $\mu$m. We defined two base distributions of different shapes for the upper and lower bound, respectively, resulting in two interpolated grading curve sets (Set A and Set B). In total, 1700 images of 34 different particle size distributions were acquired. Example images of the data set and the corresponding grading curves are shown in the figure below. ![Example images and grading curves of the finedata set](https://data.uni-hannover.de/dataset/ecb0bf04-84c8-45b1-8a43-044f3f80d92c/resource/c56f4298-9663-457f-aaa7-0ba113fec4c9/download/examplefine.png " ") ## Related publications: If you make use of the proposed data, please cite. * **Coenen, M., Beyer, D., and Haist, M., 2023**: Granulometry Transformer: Image-based Granulometry of Concrete Aggregate for an automated Concrete Production Control. In: Proceedings of the European Conference on Computing in Construction (EC3), doi: 10.35490/EC3.2023.223.

Changed value of field source_metadata_modified to 2024-01-03T14:33:50.433020 in Deep Granulometry

              
    
          
          
        
        
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            5   "doi": "10.25835/61y9peiq", 6   "doi": "10.25835/61y9peiq",
            6   "doi_date_published": "2022-11-23", 7   "doi_date_published": "2022-11-23",
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            15   "license_title": "CC-BY-NC-3.0", 18   "license_title": "CC-BY-NC-3.0",
            16   "maintainer": "Max Coenen", 19   "maintainer": "Max Coenen",
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            20   "name": "luh-deep-granulometry", 23   "name": "luh-deep-granulometry",
            21   "notes": "This repository contains the data related to the paper **  24   "notes": "This repository contains the data related to the paper ** 
            n 22 \"Deep Granulometry: Image based estimation of concrete aggregate size  n 25 \"Granulometry transformer: image-based granulometry of concrete 
            23 distributions using deep learning\" ** where a deep learning based  26 aggregate for an automated concrete production control\" ** where a 
            24 method is proposed for the image based determination of concrete  27 deep learning based method is proposed for the image based 
            25 aggregate grading curves (cf. video).\r\n\r\n[![Watch the  28 determination of concrete aggregate grading curves (cf. 
            29 video).\r\n\r\n[![Watch the 
            26 enen.github.io/resources/images/2023_DeepGranulometry.mp4)\r\n\r\nMore  30 enen.github.io/resources/images/2023_DeepGranulometry.mp4)\r\n\r\nMore 
            27 specifically, the data set consists of images showing concrete  31 specifically, the data set consists of images showing concrete 
            28 aggregate particles and reference data of the particle size  32 aggregate particles and reference data of the particle size 
            29 distribution (grading curves) associated to each image. \r\nIt is  33 distribution (grading curves) associated to each image. \r\nIt is 
            30 distinguished between the **CoarseAggregateData** and the  34 distinguished between the **CoarseAggregateData** and the 
            31 **FineAggregateData**. \r\n&nbsp;\r\n&nbsp;\r\n\r\n\r\n\r\n# Coarse  35 **FineAggregateData**. \r\n&nbsp;\r\n&nbsp;\r\n\r\n\r\n\r\n# Coarse 
            32 Aggregate Data\r\nThe __coarse__ data consists of aggregate samples  36 Aggregate Data\r\nThe __coarse__ data consists of aggregate samples 
            33 with different particles sizes ranging from 0.1 mm to 32 mm. The  37 with different particles sizes ranging from 0.1 mm to 32 mm. The 
            34 grading curves are designed by linearly  interpolation between a very  38 grading curves are designed by linearly  interpolation between a very 
            35 fine and a very coarse distribution for three variants with maximum  39 fine and a very coarse distribution for three variants with maximum 
            36 grain sizes of 8 mm, 16 mm, and 32 mm, respectively. For each variant,  40 grain sizes of 8 mm, 16 mm, and 32 mm, respectively. For each variant, 
            37 we designed eleven grading curves, resulting in a total number 33,  41 we designed eleven grading curves, resulting in a total number 33, 
            38 which are shown in the figure below. For each sample, we acquired 50  42 which are shown in the figure below. For each sample, we acquired 50 
            39 images with a GSD of 0.125 mm, resulting in a data set of 1650 images  43 images with a GSD of 0.125 mm, resulting in a data set of 1650 images 
            40 in total. Example images for a subset of the\r\ngrading curves of this  44 in total. Example images for a subset of the\r\ngrading curves of this 
            41 data set are shown in the following figure.\r\n\r\n![Example images  45 data set are shown in the following figure.\r\n\r\n![Example images 
            42 and grading curves of the coarse data  46 and grading curves of the coarse data 
            43 source/8cb30616-5b24-4028-9c1d-ea250ac8ac84/download/examplecoarse.png  47 source/8cb30616-5b24-4028-9c1d-ea250ac8ac84/download/examplecoarse.png 
            44 \" \")\r\n\r\n&nbsp;\r\n&nbsp;\r\n\r\n# Fine Aggregate Data\r\nSimilar  48 \" \")\r\n\r\n&nbsp;\r\n&nbsp;\r\n\r\n# Fine Aggregate Data\r\nSimilar 
            45 to the previous data set, the __fine__ data set contains grading  49 to the previous data set, the __fine__ data set contains grading 
            46 curves for the fine\r\nfraction of concrete aggregate of 0 to 2 mm  50 curves for the fine\r\nfraction of concrete aggregate of 0 to 2 mm 
            47 with a GSD of 28.5 $\\mu$m.\r\nWe defined two base distributions of  51 with a GSD of 28.5 $\\mu$m.\r\nWe defined two base distributions of 
            48 different shapes for the upper and lower bound, respectively,  52 different shapes for the upper and lower bound, respectively, 
            49 resulting in two interpolated grading curve sets (Set A and Set B). In  53 resulting in two interpolated grading curve sets (Set A and Set B). In 
            50 total, 1700\r\nimages of 34 different particle size distributions were  54 total, 1700\r\nimages of 34 different particle size distributions were 
            51 acquired. Example images of the data set and the corresponding grading  55 acquired. Example images of the data set and the corresponding grading 
            52 curves are shown in the figure below.\r\n![Example images and grading  56 curves are shown in the figure below.\r\n![Example images and grading 
            53 curves of the finedata  57 curves of the finedata 
            54 resource/c56f4298-9663-457f-aaa7-0ba113fec4c9/download/examplefine.png  58 resource/c56f4298-9663-457f-aaa7-0ba113fec4c9/download/examplefine.png 
            55 \" \")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n## Related  59 \" \")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n## Related 
            n 56 publications:\r\nIf you make use of the proposed data, please cite one  n 60 publications:\r\nIf you make use of the proposed data, please cite. 
            57 of the publication listed below. \r\n\r\n* **Coenen, M., Beyer, D.,  61 \r\n\r\n* **Coenen, M., Beyer, D., and Haist, M., 2023**: Granulometry 
            58 and Haist, M., 2023**: Granulometry Transformer: Image-based  62 Transformer: Image-based Granulometry of Concrete Aggregate for an 
            59 Granulometry of Concrete Aggregate for an automated Concrete  
            60 Production Control. In: Proceedings of the European Conference on  63 automated Concrete Production Control. In: Proceedings of the European 
            61 Computing in Construction (EC3).\r\n\r\n* **Coenen, M., Beyer, D.,  64 Conference on Computing in Construction (EC3), doi: 
            62 Ponick, A., Heipke, C. and Haist, M., 2023**: Deep Granulometry: Image  65 10.35490/EC3.2023.223.\r\n\r\n",
            63 based estimation of concrete aggregate size distributions using deep  
            64 learning. _To be published_.\r\n\r\n## Source Code \r\nSource code for  
            65 CNN based prediction of grading curves using the data set can be found  
            66 on github under following  
            67 [Link](https://github.com/MaximilianCoenen/DeepGranulometry.git).", 
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