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 40 for “"R-CNN"”.
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G A N mask R-CNN : instance semantic segmentation benefits from generative adversarial networks
… the true distribution, and a ConvNet like Mask R-CNN is an implicit model that infers the true distribution. In GANs terms, Mask R-CNN is the generator who reconstructs a mask as the fake one. We then send the fake mask and the real (ground truth) one to a discriminator (critic). By playing a …
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Automated detection and classification of red roman in unconstrained underwater environments using Mask R-CNN
… 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 been …
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Low-Complexity Structured Neural Networks and Their Usage in Image and Signal Processing
… achieving at least 97% FLOP reduction over CNNs, R-CNN, Faster R-CNN, DCT-Net, and YOLOv11s, and 50% parameter reduction when compared to CNNs, R-CNN, and Faster R-CNN, alongside the lowest inference time of order 10<sup>−4</sup> seconds on the DigiFace1M dataset, compared among all …
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Dynamic object tracking and classification from a moving platform
… 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 objects in …
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Detection of Texture-less Occluded Objects Using Deep Convolutional Neural Networks
… Region based Convolution Network (Faster R-CNN) to accomplish the main objective. Mobilenet is the base i model in SSD; whereas, Inception is the base model in Faster R-CNN. SSD is superior than Faster R-CNN in terms of speed, but inferior in terms of average precision. A large number of …
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Addressing Occlusion in Panoptic Segmentation
… 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 Cityscapes …
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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 AHydraFPN …
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Investigating automated bird detection from webcams using machine learning
… single-shot detector (SSD) and Faster R-CNN in combination with MobileNet-V2, ResNet50, ResNet101, ResNet152, and Inception ResNet-V2 feature extractors were studied and evaluated. Through the use of transfer learning, all the models were initialized using weights pre-trained on the MS …
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Machine Learning for Lifespan Inference from Time-Lapse Microfluidic Images of Dividing Yeast 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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Skraidančių mikro objektų sekimas /
… to choose three different models - Faster R-CNN, YOLOv4 and YOLOv5. These were then trained with the same dataset for drone recognition and compared by the selected metrics - time it took to train them, mAP and recall. Judging by these metrics YOLOv5 achieved the best results and therefore …
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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 dissertation …
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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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Feasibility of Neural Networks for Maritime Visual Detection on a Mobile Platform
… several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are …
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Advancing Precision Agriculture Through AI and Statistical Modeling: Transforming Crop and Livestock Management
… YOLOv9 and InternImage with Cas- cade R-CNN for precise image segmentation. Advanced feature extraction methods utilizing ResNet, DenseNet, and ResNeXt are employed to develop ML and deep learning (DL) mod- els, providing a non-invasive alternative to traditional weight measurement …
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Object Detection and Size Determination of Pineapple Fruit at a Juicing Factory
… 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 detection. The best-performing detector made use of MS COCO …
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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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Multi-classification and object detection in intelligent manufacturing
… algorithms were adopted and compared: Faster R-CNN, YOLO v4, and YOLO v5. They achieved mAP@0.5 of 94.31%, 95.22%, 75.5% respectively, and recall rates of 67%, 89%, 73.5% respectively, which demonstrated promising results for applications on the production line. However, the results were …
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Go-Green or Go-Home : optimizing a real-time traffic monitoring system
… were also performed using the Faster R-CNN object detection algorithm but only with the distance variable to decide if a license plate should be read. The results from the experiments indicate that using the Faster R-CNN object detection algorithm is too slow for the desired purpose. …
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Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
… the Region Proposal Network (RPN) by Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps to predict the correct type of tumor with better accuracy about 99%. and classifying by using Convolutional Neural Networks (CNN). The deeper …
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Unsupervised Machine Learning Application for the Identification of Kimberlite Ore Facie using Convolutional Neural Networks and Deep Embedded Clustering
… 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 pipeline - utilizing an unsupervised clustering method …
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