Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/obb/dota-v2.md

@@ -6,7 +6,7 @@ keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes
 
 # DOTA Dataset with OBB
 
-[DOTA](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).
+[DOTA](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing [object detection](https://www.ultralytics.com/glossary/object-detection) in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).
 
 ![DOTA classes visual](https://github.com/ultralytics/docs/releases/download/0/dota-classes-visual.avif)
 
@@ -128,7 +128,7 @@ Having a glance at the dataset illustrates its depth:
 
 ![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/instances-DOTA.avif)
 
-- **DOTA examples**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented Bounding Box annotations, capturing objects in their natural orientation.
+- **DOTA examples**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented [Bounding Box](https://www.ultralytics.com/glossary/bounding-box) annotations, capturing objects in their natural orientation.
 
 The dataset's richness offers invaluable insights into object detection challenges exclusive to aerial imagery.
 

docs/en/datasets/obb/dota8.md

@@ -8,7 +8,7 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train
 
 ## Introduction
 
-[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
+[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented [object detection](https://www.ultralytics.com/glossary/object-detection) dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
 This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLOv8n-obb model on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -121,4 +121,4 @@ Mosaicing combines multiple images into one during training, increasing the vari
 
 ### Why should I use Ultralytics YOLOv8 for object detection tasks?
 
-Ultralytics YOLOv8 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), instance segmentation, and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLOv8 documentation](https://github.com/ultralytics/ultralytics).
+Ultralytics YOLOv8 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLOv8 documentation](https://github.com/ultralytics/ultralytics).

docs/en/datasets/obb/index.md

@@ -6,7 +6,7 @@ keywords: Oriented Bounding Box, OBB Datasets, YOLO, Ultralytics, Object Detecti
 
 # Oriented Bounding Box (OBB) Datasets Overview
 
-Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
+Training a precise [object detection](https://www.ultralytics.com/glossary/object-detection) model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
 
 ## Supported OBB Dataset Formats
 
@@ -18,7 +18,7 @@ The YOLO OBB format designates bounding boxes by their four corner points with c
 class_index x1 y1 x2 y2 x3 y3 x4 y4
 ```
 
-Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation.
+Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the [bounding box](https://www.ultralytics.com/glossary/bounding-box)'s center point (xy), width, height, and rotation.
 
 <p align="center"><img width="800" src="https://github.com/ultralytics/docs/releases/download/0/obb-format-examples.avif" alt="OBB format examples"></p>
 
@@ -129,7 +129,7 @@ Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO
         yolo obb train data=your_dataset.yaml model=yolov8n-obb.yaml epochs=100 imgsz=640
         ```
 
-This ensures your model leverages the detailed OBB annotations for improved detection accuracy.
+This ensures your model leverages the detailed OBB annotations for improved detection [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 ### What datasets are currently supported for OBB training in Ultralytics YOLO models?