diff --git a/docs/detection/utils/masks.md b/docs/detection/utils/masks.md
index 9e53a6baa..99097bef6 100644
--- a/docs/detection/utils/masks.md
+++ b/docs/detection/utils/masks.md
@@ -22,3 +22,9 @@ status: new
 </div>
 
 :::supervision.detection.utils.masks.contains_multiple_segments
+
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.masks.filter_segments_by_distance">filter_segments_by_distance</a></h2>
+</div>
+
+:::supervision.detection.utils.masks.filter_segments_by_distance
diff --git a/supervision/__init__.py b/supervision/__init__.py
index a70dd20fe..ccd272930 100644
--- a/supervision/__init__.py
+++ b/supervision/__init__.py
@@ -87,6 +87,7 @@
     calculate_masks_centroids,
     contains_holes,
     contains_multiple_segments,
+    filter_segments_by_distance,
     move_masks,
 )
 from supervision.detection.utils.polygons import (
@@ -219,6 +220,7 @@
     "draw_text",
     "edit_distance",
     "filter_polygons_by_area",
+    "filter_segments_by_distance",
     "fuzzy_match_index",
     "get_coco_class_index_mapping",
     "get_polygon_center",
diff --git a/supervision/detection/utils/masks.py b/supervision/detection/utils/masks.py
index c5cfee017..35896f20c 100644
--- a/supervision/detection/utils/masks.py
+++ b/supervision/detection/utils/masks.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from typing import Literal
+
 import cv2
 import numpy as np
 import numpy.typing as npt
@@ -260,3 +262,139 @@ def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
     resized_masks = masks[:, yv, xv]
 
     return resized_masks.reshape(masks.shape[0], new_height, new_width)
+
+
+def filter_segments_by_distance(
+    mask: npt.NDArray[np.bool_],
+    absolute_distance: float | None = 100.0,
+    relative_distance: float | None = None,
+    connectivity: int = 8,
+    mode: Literal["edge", "centroid"] = "edge",
+) -> npt.NDArray[np.bool_]:
+    """
+    Keep the largest connected component and any other components within a distance
+    threshold.
+
+    Distance can be absolute in pixels or relative to the image diagonal.
+
+    Args:
+        mask: Boolean mask HxW.
+        absolute_distance: Max allowed distance in pixels to the main component.
+            Ignored if `relative_distance` is provided.
+        relative_distance: Fraction of the diagonal. If set, threshold = fraction * sqrt(H^2 + W^2).
+        connectivity: Defines which neighboring pixels are considered connected.
+            - 4-connectedness: Only orthogonal neighbors.
+              ```
+              [ ][X][ ]
+              [X][O][X]
+              [ ][X][ ]
+              ```
+            - 8-connectedness: Includes diagonal neighbors.
+              ```
+              [X][X][X]
+              [X][O][X]
+              [X][X][X]
+              ```
+            Default is 8.
+        mode: Defines how distance between components is measured.
+            - "edge": Uses distance between nearest edges (via distance transform).
+            - "centroid": Uses distance between component centroids.
+
+    Returns:
+        Boolean mask after filtering.
+
+        Examples:
+        ```python
+        import numpy as np
+        import supervision as sv
+
+        mask = np.array([
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        ], dtype=bool)
+
+        sv.filter_segments_by_distance(
+            mask,
+            absolute_distance=2,
+            mode="edge",
+            connectivity=8
+        ).astype(int)
+
+        # np.array([
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        #     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        # ], dtype=bool)
+
+        # The nearby 2×2 block at columns 6–7 is kept because its edge distance
+        # is within 2 pixels. The distant block at columns 9-10 is removed.
+        ```
+    """  # noqa E501 // docs
+    if mask.dtype != bool:
+        raise TypeError("mask must be boolean")
+
+    height, width = mask.shape
+    if not np.any(mask):
+        return mask.copy()
+
+    image = mask.astype(np.uint8)
+    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
+        image, connectivity=connectivity
+    )
+
+    if num_labels <= 1:
+        return mask.copy()
+
+    areas = stats[1:, cv2.CC_STAT_AREA]
+    main_label = 1 + int(np.argmax(areas))
+
+    if relative_distance is not None:
+        diagonal = float(np.hypot(height, width))
+        threshold = float(relative_distance) * diagonal
+    else:
+        threshold = float(absolute_distance)
+
+    keep_labels = np.zeros(num_labels, dtype=bool)
+    keep_labels[main_label] = True
+
+    if mode == "centroid":
+        differences = centroids[1:] - centroids[main_label]
+        distances = np.sqrt(np.sum(differences**2, axis=1))
+        nearby = 1 + np.where(distances <= threshold)[0]
+        keep_labels[nearby] = True
+    elif mode == "edge":
+        main_mask = (labels == main_label).astype(np.uint8)
+        inverse = 1 - main_mask
+        distance_transform = cv2.distanceTransform(inverse, cv2.DIST_L2, 3)
+        for label in range(1, num_labels):
+            if label == main_label:
+                continue
+            component = labels == label
+            if not np.any(component):
+                continue
+            min_distance = float(distance_transform[component].min())
+            if min_distance <= threshold:
+                keep_labels[label] = True
+    else:
+        raise ValueError("mode must be 'edge' or 'centroid'")
+
+    return keep_labels[labels]
diff --git a/test/detection/utils/test_masks.py b/test/detection/utils/test_masks.py
index 2097f6082..b41f208ed 100644
--- a/test/detection/utils/test_masks.py
+++ b/test/detection/utils/test_masks.py
@@ -10,6 +10,7 @@
     calculate_masks_centroids,
     contains_holes,
     contains_multiple_segments,
+    filter_segments_by_distance,
     move_masks,
 )
 
@@ -500,3 +501,228 @@ def test_contains_multiple_segments(
     with exception:
         result = contains_multiple_segments(mask=mask, connectivity=connectivity)
         assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "mask, connectivity, mode, absolute_distance, relative_distance, expected_result, exception",  # noqa: E501
+    [
+        # single component, unchanged
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "edge",
+            2.0,
+            None,
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+        # two components, edge distance 2, kept with abs=1
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "edge",
+            2.0,
+            None,
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 1, 1, 1, 0, 1],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+        # centroid mode, far centroids, dropped with small relative threshold
+        (
+            np.array(
+                [
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1],
+                    [0, 0, 0, 1, 1, 1],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "centroid",
+            None,
+            0.3,  # diagonal ~8.49, threshold ~2.55, centroid gap ~4.24
+            np.array(
+                [
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+        # centroid mode, larger relative threshold, kept
+        (
+            np.array(
+                [
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1],
+                    [0, 0, 0, 1, 1, 1],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "centroid",
+            None,
+            0.6,  # diagonal ~8.49, threshold ~5.09, centroid gap ~4.24
+            np.array(
+                [
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1],
+                    [0, 0, 0, 1, 1, 1],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+        # empty mask
+        (
+            np.zeros((4, 4), dtype=bool),
+            4,
+            "edge",
+            2.0,
+            None,
+            np.zeros((4, 4), dtype=bool),
+            DoesNotRaise(),
+        ),
+        # full mask
+        (
+            np.ones((4, 4), dtype=bool),
+            8,
+            "centroid",
+            None,
+            0.2,
+            np.ones((4, 4), dtype=bool),
+            DoesNotRaise(),
+        ),
+        # two components, pixel distance = 2, kept with abs=2
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "edge",
+            2.0,  # was 1.0
+            None,
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 1, 1, 1, 0, 1, 1, 1],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+        # two components, pixel distance = 3, dropped with abs=2
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0, 0, 1, 1],
+                    [0, 1, 1, 1, 0, 0, 0, 1, 1],
+                    [0, 1, 1, 1, 0, 0, 0, 1, 1],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            8,
+            "edge",
+            2.0,  # keep threshold below 3 so the right blob is removed
+            None,
+            np.array(
+                [
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0, 0, 0, 0],
+                    [0, 1, 1, 1, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                ],
+                dtype=bool,
+            ),
+            DoesNotRaise(),
+        ),
+    ],
+)
+def test_filter_segments_by_distance_sweep(
+    mask: npt.NDArray,
+    connectivity: int,
+    mode: str,
+    absolute_distance: float | None,
+    relative_distance: float | None,
+    expected_result: npt.NDArray | None,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = filter_segments_by_distance(
+            mask=mask,
+            connectivity=connectivity,
+            mode=mode,  # type: ignore[arg-type]
+            absolute_distance=absolute_distance,
+            relative_distance=relative_distance,
+        )
+        assert np.array_equal(result, expected_result)