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7 results for “Mask R-CNN”
Evaluation of Mask R-CNN Model for Counting Reproductive Structures of Six Plant Species 1895-2018
Phenology––the timing of life-history events––is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current common practice of manually harvesting data from individual specimens greatly restricts our ability to scale data collection to entire collections. Recent investigations have demonstrated that machine-learning models can facilitate data collection from herbarium specimens. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., flowering or not). Here, we use crowd-sourced phenological data of numbers of buds, flowers, and fruits of more than 3000 specimens of six common wildflower species of the eastern United States (Anemone canadensis, A. hepatica, A. quinquefolia, Trillium erectum, T. grandiflorum, and T. undulatum} to train a model using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of reproductive stage with greater than 90% accuracy. Segmenting and counting features were also successful, but accuracy varied with phenological stage and taxon. Counting buds was significantly more accurate than flowers or fruits. Moreover, botanical experts provided more reliable data than either crowd-sourcers or our Mask R-CNN model, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, providing high-quality data with which to study responses of plants to ongoing climatic change.
Hail Event on 2022-06-28 in Locarno-Monti (TI), Switzerland: Drone Photogrammetry Imagery, Mask R-CNN Model and Analysis Data of Hailstones
<p>This hail data collection belongs to a drone hail survey performed on 2022-06-28 in Locarno-Monti (TI, Switzerland). The supercell reached the location around 07:50 UTC in the morning. Only one photogrammetry flight could be performed and thus no estimation of the hail melting process is available. The orthophoto is masked to ignore parts where detection of hail is unwanted.</p> <p> </p> <p>Expert 1 (lai, mlainer), Expert 2 (jtm), Expert 3 (por, jportmann)</p>
Dataset: Mask R-CNN Based C. Elegans Detection with a DIY Microscope
<p>The dataset consists of images of C. elegans in Petri Dish that were captured at a frequency of 1 Hz at 3280 × 2464 pixels via a Raspberry Pi based DIY Microscope. Further details of the recording setup and the dataset can be found in the corresponding article.</p> <p>Up on use, please cite the following article <a href="https://doi.org/10.3390/bios11080257">https://doi.org/10.3390/bios11080257</a> such as:</p> <p>Fudickar, S.; Nustede, E.J.; Dreyer, E.; Bornhorst, J. Mask R-CNN Based C. Elegans Detection with a DIY Microscope. <em>Biosensors</em> <strong>2021</strong>, <em>11</em>, 257. https://doi.org/10.3390/bios11080257</p> <p> </p> <p><br> </p>
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</p>
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Mask R-CNN on NYUv2
<p><strong>Mask R-CNN on NYUv2</strong></p> <p>This repository mainly contains information from the execution of the <a href="https://github.com/facebookresearch/maskrcnn-benchmark">Mask R-CNN network </a>[1] on images from the <a href="https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html">NYUv2 dataset</a> [2] as well as additional metadata. It was created for analyzing the output of Mask R-CNN and post-processing it using contextual information for improving its performance. This work has been carried out by <a href="http://mapir.isa.uma.es/jotaraul">Dr. Jose-Raul Ruiz-Sarmiento</a> (MAPIR group, University of Málaga) and <a href="https://lishuda.wordpress.com/">Dr. Shuda Li</a> (AVG group, University of Oxford) in the scope of the European project<a href="http://www.movecare-project.eu/"> MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder</a> (Ref: 732158).</p> <p>Concretely, <strong>this repository includes</strong>:</p> <p>- metadata:<br> + coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2.<br> + coco_object_categories.txt: Object categories considered in COCO dataset.<br> + nyu_object_categories.txt: Object categories used in NYUv2 dataset.<br> + nyu_scene_categories.txt: Scene categories considered in NYUv2.<br> + objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects.</p> <p>- nyu_content:<br> + masks_in_X (Where X is the image index)<br> - Y.png: Where Y is the object index in the image, represents the binary mask of that object.<br> - pixels_labelled.png: Binary mask indicating the labelled pixels in image X.<br> + bboxesX.txt: Where X is the image index, includes the ground truth bounding boxes of the objects in it. Format is: min_x min_y max_x max_y.</p> <p>- preds:<br> + X: Where X is the image index.<br> - Y.png: Where Y is the object index in the image, as detected by Mask R-CNN. Binary image containing the mask of such detected object.<br> + X.txt: Where X is the image index. File containing the objects detected by Mask R-CNN, including: idx class score min_x min_y max_x max_y masks_file, being min_x min_y max_x and max_y bounding box information, while masks_file refers to X/Y.png as described above.<br> + result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3.<br> + gt_iou_X: Where X is the image index.<br> - Y: Where Y is the index of the detected object.<br> + Z.png Where Z is the index of the object in the ground truth. Image showing the masks of both objects, Y and Z, for visually checking their overlapping.<br> - Y.txt: Where Y is the index of the detected object. File containing:<br> + The intersection ratio of the object mask Y with the labelled part of the image.<br> + The IoU value for the mask of object Y and those of ground truth objects.<br> <br> <br> <strong>References:</strong></p> <p>[1] He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.<br> [2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. "Indoor segmentation and support inference from rgbd images." In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.</p>
Mask R-CNN for characterization of yardang landforms
<p>This includes the source code and datasets for the automated characterization of yardang landforms using Mask R-CNN. Codes and datasets used in the manuscript will be submitted to the Journal of Geophysical Research: Earth Surface are included. The readability of codes and other documents will be updated soon.</p>
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OpenNeuro
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