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.
Results
Showing 1 to 7 of 7 for “"YOLOv11"”.
-
Design and optimization of an embedded machine learning image processing system with a Linux SOC for large-scale livestock tallying
… models, encompassing the YOLOv9, YOLOv10, and YOLOv11 and their size variants. NVIDIA’s TensorRT acceleration framework is used to optimise these custom-trained architectures for FP32, FP16, and INT8 quantization formats, enabling further performance assessment on the selected NVIDIA Jetson …
-
AI-Based Framework for Identifying Wood Burning Appliances through Chimney Recognition
… rural areas, we trained object detection models (YOLOv11) and integrated them with Vision Language Models (VLMs) for semantic verification. This two-stage pipeline substantially improves detection accuracy over YOLO alone. In the urban Fall Creek neighborhood, the YOLO+VLMs approach achieved an …
-
Gaze-Aware Driver Maneuver Prediction Using Object Detection and Sequential Deep Learning Models for Advanced Driver Assistance Systems
… detection was performed using custom-trained YOLOv11 models, whose outputs were aligned with gaze data to create object-in-gaze features. These features, combined with vehicle sensor data, were used to train and evaluate three deep learning architectures: Gated Recurrent Units (GRU), Long …
-
Sensing and Predicting Urban Rail Platform Crowding Using Emerging Data Sources
… vision methods, namely object detection (YOLOv11, RT-DETRv2) and head counting (APGCC), crowd-level classification (Crowd-ViT), and semantic image segmentation (DeepLabV3), we demonstrate that estimated counts from calibrated image segmentation maps enable accurate real-time estimation of …
-
Deep Learning in Oesophageal Cancer Development: Integrating Multi-Stain Histopathology Images of the Capsule Sponge
… workflow. Using transfer learning with YOLOv11, I trained seg- mentation models for H&E and IHC images, followed by classification models for p53 (positive, equivocal, negative), TFF3 (positive, negative), and H&E glands (normal, atypical, dysplastic). Trained on enriched subsets of the …
-
Using machine learning methods to estimate spruce tree crown and DBH from aerial imagery
… aerial images. Compared to the second method YOLOv11 that uses instance segmentation to segment the trees. A study is conducted to showcase a relationship between TCD and DBH of the field measurements. This linear relationship can be used to estimate DBH out of TCD and then could futher …
-
Grape yield estimation in commercial vineyards using artificial intelligence, image analysis, and proximal RGB sensing
… fenológicos con imágenes RGB convencionales. YOLOv11-Classification ofreció el mejor equilibrio entre precisión y eficiencia, mientras que ResNet-34 y Vision Transformer fueron competitivos con mayor coste o métricas menos equilibradas. La clasificación automática, esencial porque la …