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 13 of 13 for “"Faster R-CNN"”.
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Detection of Texture-less Occluded Objects Using Deep Convolutional Neural Networks
… has opted Single Shot Detector (SSD) and Faster 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 …
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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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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 …
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Skraidančių mikro objektų sekimas /
… was decided 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 …
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Feasibility of Neural Networks for Maritime Visual Detection on a Mobile Platform
… investigate 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 …
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Multi-classification and object detection in intelligent manufacturing
… detection 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 …
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Go-Green or Go-Home : optimizing a real-time traffic monitoring system
… Experiments 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 …
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Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
… segment 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 …
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Computer vision based corn kernel quality evaluation: Traditional versus machine learning
… state-of-the-art detectors, specifically Faster R-CNN and Retinanet. We collected two databases for both methods separately: (1) Images of many corn kernel batches containing different percentages of good corn kernels vs.foreign matter randomly placed on a flat surface were taken as both …
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Adaptation des architectures ADDA et semi-ADDA pour la détection d'objets par apprentissage profond sur les images satellites THR
… l’émergence des réseaux de neurones convolutifs (CNNs, convolutional neural networks), de nombreuses approches de détection d’objets basées sur les CNNs ont été proposées dans les dernières années améliorant considérablement les performances obtenues par les algorithmes traditionnels. Or, bien que …
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Disaster and infrastructure scene understanding
… adopted. Several convolutional neural network (CNN) models based on the seminal Faster R-CNN models are tested and observed with high accuracy. In the second situation, I explored the possibility of learning from crowd-based mobile images for understanding complex disaster disasters resulting …
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Image-based deep learning approaches for plant phenotyping
… identified using a deep learning model based on Faster Region-based 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 …
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Deep learning-based seagrass detection and classification from underwater digital images
… in particular Convolutional Neural Networks (CNNs), have rapidly become a method of choice for analysing seagrass image data. Deep learning-based seagrass classification and detection are very challenging due to the limited labelled data, intraclass similarities between species, lighting …