diff --git a/docs/datasets/utils.md b/docs/datasets/utils.md
index 6be56303f..c4cf6503e 100644
--- a/docs/datasets/utils.md
+++ b/docs/datasets/utils.md
@@ -16,3 +16,9 @@ status: new
 </div>
 
 :::supervision.dataset.utils.mask_to_rle
+
+<div class="md-typeset">
+  <h2><a href="#supervision.dataset.utils.merge_polygons">merge_polygons</a></h2>
+</div>
+
+:::supervision.dataset.utils.merge_polygons
diff --git a/supervision/dataset/formats/coco.py b/supervision/dataset/formats/coco.py
index 353e33f5e..6b7d2bcf5 100644
--- a/supervision/dataset/formats/coco.py
+++ b/supervision/dataset/formats/coco.py
@@ -11,6 +11,7 @@
     approximate_mask_with_polygons,
     map_detections_class_id,
     mask_to_rle,
+    merge_polygons,
     rle_to_mask,
 )
 from supervision.detection.core import Detections
@@ -74,6 +75,8 @@ def coco_annotations_to_masks(
                 resolution_wh=resolution_wh,
             )
             if image_annotation["iscrowd"]
+            else merge_polygons(image_annotation["segmentation"], resolution_wh)
+            if len(image_annotation["segmentation"]) > 1
             else polygon_to_mask(
                 polygon=np.reshape(
                     np.asarray(image_annotation["segmentation"], dtype=np.int32),
diff --git a/supervision/dataset/utils.py b/supervision/dataset/utils.py
index 32ece6bf1..33ca85b84 100644
--- a/supervision/dataset/utils.py
+++ b/supervision/dataset/utils.py
@@ -13,6 +13,7 @@
     approximate_polygon,
     filter_polygons_by_area,
     mask_to_polygons,
+    polygon_to_mask,
 )
 
 T = TypeVar("T")
@@ -46,6 +47,59 @@ def approximate_mask_with_polygons(
     ]
 
 
+def merge_polygons(
+    polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
+) -> np.ndarray:
+    """
+    Merge polygons (in the form of vertices) within the segmentation list
+    of a COCO annotation.
+
+    Args:
+        polygons (List[np.ndarray]): A nested list of vertices, with each
+            nested list representing one polygon
+        resolution_wh (Tuple[int, int]): The width (w) and height (h)
+            of the desired binary mask.
+
+    Returns:
+        np.ndarray: The generated 2D mask, where the polygon is marked with
+            `1`'s and the rest is filled with `0`'s.
+
+    Examples:
+        ```python
+        import supervision as sv
+
+        sv.merge_polygons([[1.0, 1.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0]], (8, 4))
+        # array([
+        #     [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+        #     [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+        #     [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+        #     [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+        # ])
+        ```
+    """
+    parent_polygon = polygons[0]
+    parent_mask = polygon_to_mask(
+        polygon=np.reshape(
+            np.asarray(parent_polygon, dtype=np.int32),
+            (-1, 2),
+        ),
+        resolution_wh=resolution_wh,
+    )
+
+    for p in polygons[1:]:
+        child_mask = polygon_to_mask(
+            polygon=np.reshape(
+                np.asarray(p, dtype=np.int32),
+                (-1, 2),
+            ),
+            resolution_wh=resolution_wh,
+        )
+
+        parent_mask = np.logical_or(parent_mask, child_mask)
+
+    return parent_mask.astype(float)
+
+
 def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
     unique_classes = set()
 
diff --git a/test/dataset/test_utils.py b/test/dataset/test_utils.py
index 41e1da5bc..569873a45 100644
--- a/test/dataset/test_utils.py
+++ b/test/dataset/test_utils.py
@@ -12,6 +12,7 @@
     map_detections_class_id,
     mask_to_rle,
     merge_class_lists,
+    merge_polygons,
     rle_to_mask,
     train_test_split,
 )
@@ -125,6 +126,63 @@ def test_merge_class_maps(
         assert result == expected_result
 
 
+@pytest.mark.parametrize(
+    "polygons, resolution_wh, expected_result, exception",
+    [
+        (
+            np.array([[1.0, 1.0, 2.0, 3.0]]),
+            (8, 4),
+            np.array(
+                [
+                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                ]
+            ),
+            DoesNotRaise(),
+        ),  # single polygon
+        (
+            np.array([[1.0, 1.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0]]),
+            (8, 4),
+            np.array(
+                [
+                    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                ]
+            ),
+            DoesNotRaise(),
+        ),  # two polygons
+        (
+            np.array(
+                [[1.0, 0.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0], [1.0, 2.0, 1.0, 2.0]]
+            ),
+            (8, 4),
+            np.array(
+                [
+                    [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+                    [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                ]
+            ),
+            DoesNotRaise(),
+        ),  # multiple polygons
+    ],
+)
+def test_merge_polygons(
+    polygons: List[np.ndarray],
+    resolution_wh: Tuple[int, int],
+    expected_result: np.ndarray,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = merge_polygons(polygons=polygons, resolution_wh=resolution_wh)
+        assert np.array_equal(result, expected_result)
+
+
 @pytest.mark.parametrize(
     "source_classes, target_classes, expected_result, exception",
     [