diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst
index 7f98fbb33c..3f129a3c2f 100644
--- a/docs/source/tutorials/index.rst
+++ b/docs/source/tutorials/index.rst
@@ -216,4 +216,4 @@ your datasets and turn your good models into *great models*.
    Zero-shot classification <zero_shot_classification.ipynb>
    Data augmentation <data_augmentation.ipynb>
    Clustering images <clustering.ipynb>
-   Detecting small objects<small_object_detection.ipynb>
+   Detecting small objects <small_object_detection.ipynb>
diff --git a/docs/source/tutorials/small_object_detection.ipynb b/docs/source/tutorials/small_object_detection.ipynb
index 34d962be00..097b27eb2a 100644
--- a/docs/source/tutorials/small_object_detection.ipynb
+++ b/docs/source/tutorials/small_object_detection.ipynb
@@ -20,7 +20,7 @@
    "source": [
     "Object detection is one of the fundamental tasks in computer vision, but detecting small objects can be particularly challenging.\n",
     "\n",
-    "In this walkthrough, you'll learn how to use a technique called SAHI (Slicing Aided Hyper Inference) in conjunction with state-of-the-art object detection models to improve the detection of small objects. We'll apply SAHI with Ultralytics' YOLOv8 model to detect small objects in the VisDrone dataset, and then evaluate these predictions to better understand how slicing impacts detection performance.\n",
+    "In this walkthrough, you'll learn how to use a technique called [SAHI (Slicing Aided Hyper Inference)](https://ieeexplore.ieee.org/document/9897990) in conjunction with state-of-the-art object detection models to improve the detection of small objects. We'll apply SAHI with Ultralytics' YOLOv8 model to detect small objects in the VisDrone dataset, and then evaluate these predictions to better understand how slicing impacts detection performance.\n",
     "\n",
     "It covers the following:\n",
     "\n",