Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"mask r-cnn"”.
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G A N mask R-CNN : instance semantic segmentation benefits from generative adversarial networks
… instance segmentation ConvNets that reconstruct masks, segmentation is often taken as its literal definition -assigning label to every pixel- for defining the loss functions. That is, using losses that compute the difference between pixels in the predicted (reconstructed) mask and the ground …
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Automated detection and classification of red roman in unconstrained underwater environments using Mask R-CNN
… of the time and cost, at a comparable accuracy. Mask R-CNN, a deep learning object recognition framework, has outperformed all previous state-of-the-art results on competitive benchmarking tasks. Despite this success, Mask R-CNN and other state-of-the-art object recognition techniques have not …
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Dynamic object tracking and classification from a moving platform
… The system combines two open-source algorithms, Mask R-CNN and ORB-SLAM. Mask R-CNN provides instance-level object detection and segmentation, while ORB-SLAM provides keypoint detection, camera tracking, and local mapping. A typical SLAM system assumes a static environment and treats dynamic …
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Addressing Occlusion in Panoptic Segmentation
… the object detection pipeline in UPSNet, a Mask R-CNN based design for panoptic segmentation. We also discuss an issue with the segmentation mask prediction of Mask R-CNN that affects overlapping instances. We perform extensive experiments and showcase results on the complex COCO and …
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Object Detection on Unmanned Arial Vehicles Dataset Using Adaptive HydraNet
… objects. We integrate AHydraNet into the Mask R-CNN's backbone by introducing a smaller module called the Adaptive Branching Network (ABN), which applies AHydraNet to all the output feature maps of the feature pyramid network. The resulting model is called AHydraFPN. The performance of …
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Machine Learning for Lifespan Inference from Time-Lapse Microfluidic Images of Dividing Yeast Cells
… cells from microfluidic images. The YOLO and Mask R-CNN are trained with yeast microfluidic images and tested for object detection, and features extraction. The results indicate that YOLO had better performance in terms of object detection and accuracy. In contrast, the Mask R-CNN had better …
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Deep Learning-Based Comprehensive Pathology Image Analysis
… patients. Furthermore, U-Net and Mask R-CNN based HD-Staining models were used to identify cell nuclei of different tissue layers in oral epithelium, and novel Onion Peeling algorithm was developed to count the cell layer numbers. Overall, the deep learning tools in this …
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Integrating Multi-Source Weather Data for Deep Learning
… along with annotations to be ingested by Mask R-CNN [6] network architecture. Finally, it passes the generated training dataset as an input for Detectron [5] software application and attempts to train network for the given 2017 and 2018 storm events.</p>
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Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory
… first detecting pineapples in each image using Mask Region-based Convolutional Neural Network (Mask R-CNN) and then extracting the pixel diameter and length measurements, and the projected areas, from the detected mask outputs. Various Mask R-CNNs were considered for the task of pineapple …
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Unsupervised Machine Learning Application for the Identification of Kimberlite Ore Facie using Convolutional Neural Networks and Deep Embedded Clustering
… The first stage involves the implementation of a Mask Region-based Convolutional Neural Network (Mask R-CNN) model with a ResNet 101 CNN backbone for instance segmentation of individual rocks from each image. These individual rock images are extracted and used for the second phase of the modelling …
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Estimating Poverty from Aerial Images Using Convolutional Neural Networks Coupled with Statistical Regression Modelling
… to demonstrate how Convolutional Neural Network (CNN) coupled with statistical regression modelling can be used to estimate poverty from aerial images supplemented with national household survey data. This provides a more frequent and automated method for updating data that can be used for policy …
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Strategies for the Characterization and Virtual Testing of SLM 316L Stainless Steel
… (SS) for quantitative analysis. A custom trained mask region-based convolution neural network (Mask R-CNN) model is used to segment cell features from scanning electron microscopy (SEM) images with an instance segmentation accuracy nearly identical to that of a human researcher, but about four …
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From Bones to Bytes
… learning methods were markedly more effective. Mask R-CNN achieved good localisation accuracy on two-dimensional images, and an adapted multi-view landmark learning pipeline for 3D bone meshes produced promising results on unseen specimens, approaching or even surpassing human repeatability …
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Development of a test suite for single object tracking algorithms in video
… algorithm we implemented the state-of-the-art Mask R-CNN object detector, which achieved an average frame rate of 1.5 fps annotating video clips in up to 4K resolution. Another algorithm then played back the annotated clips to the user such that incorrect object detections could be rooted out …
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Machine-Learning based Ship Traffic Prediction in the Suez Canal
… factor model. The image detection model employs Mask R-CNN, a deep-learning neural network, and the correction factor model utilizes Long Short-Term Memory (LSTM), a recurrent neural network, to train on historical AIS data. Results of the 2-stage model using LSTM demonstrate positive indication …
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Automated Building Extraction from Remote Sensing Imagery Using Deep Learning
… building extraction method based on PolygonCNN. The proposed model consists of a segmentation module to predict building segmentations and a regularization module to regularize the building contours traced from the building segmentation results. First, an upgraded Mask R-CNN model, which is …
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Image-based deep learning approaches for plant phenotyping
… Convolutional Neural Network (Faster R-CNN) with the pre-trained VGG-16 as backbone. The model was trained on root cross-section images of roots, where the traits of interest were manually annotated as rectangular bounding boxes using the LabelImg tool. The traits were also predicted as …
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Understanding tropical forest dynamics through remote sensing and deep learning
… patterns based on convolutional neural networks (CNNs), working with freely accessible data to successfully forecast spatiotemporal patterns in the Southern Peruvian Amazon. Predicting the location of deforestation is difficult as it results from complex interactions within human-ecological …
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Inference of River Hydrodynamics and Ice Processes from Close-Range Remote Sensing
… in the first axe. The first algorithm is IceMaskNet, a novel river ice detection and segmentation algorithm based on an improved version of the Mask R-CNN. The algorithm has been successfully applied to both aerial and fixed shore-based imagery for river ice detection and classification, …
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Multivariate Analysis in Research and Industrial Environments
L'abstract è presente nell'allegato / the abstract is in the attachment