diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index 7e8a4ee9..95d1fc26 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -355,24 +355,25 @@ def process_roboflow_result(
         x_max = x_min + width
         y_max = y_min + height
 
-        xyxy.append([x_min, y_min, x_max, y_max])
-        class_id.append(class_list.index(prediction["class"]))
-        confidence.append(prediction["confidence"])
-
         if "points" not in prediction:
-            continue
-
-        if len(prediction["points"]) >= 3:
-            polygon = np.array(
-                [[point["x"], point["y"]] for point in prediction["points"]], dtype=int
-            )
-
+            xyxy.append([x_min, y_min, x_max, y_max])
+            class_id.append(class_list.index(prediction["class"]))
+            confidence.append(prediction["confidence"])
+        elif len(prediction["points"]) >= 3:
+            polygon = np.array([
+                [point["x"], point["y"]]
+                for point
+                in prediction["points"]
+            ], dtype=int)
             mask = polygon_to_mask(polygon, resolution_wh=(image_width, image_height))
+            xyxy.append([x_min, y_min, x_max, y_max])
+            class_id.append(class_list.index(prediction["class"]))
+            confidence.append(prediction["confidence"])
             masks.append(mask)
 
-    xyxy = np.array(xyxy)
-    confidence = np.array(confidence)
-    class_id = np.array(class_id).astype(int)
+    xyxy = np.array(xyxy) if len(xyxy) > 0 else np.empty((0, 4))
+    confidence = np.array(confidence) if len(confidence) > 0 else np.empty(0)
+    class_id = np.array(class_id).astype(int) if len(class_id) > 0 else np.empty(0)
     masks = np.array(masks, dtype=bool) if len(masks) > 0 else None
 
     return xyxy, confidence, class_id, masks
diff --git a/test/detection/test_utils.py b/test/detection/test_utils.py
index b096fd08..316f23e5 100644
--- a/test/detection/test_utils.py
+++ b/test/detection/test_utils.py
@@ -12,6 +12,10 @@ from supervision.detection.utils import (
 )
 
 
+TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
+TEST_MASK[:, 300:351, 200:251] = True
+
+
 @pytest.mark.parametrize(
     "predictions, iou_threshold, expected_result, exception",
     [
@@ -259,9 +263,17 @@ def test_filter_polygons_by_area(
     "roboflow_result, class_list, expected_result, exception",
     [
         (
-            {"predictions": [], "image": {"width": 1000, "height": 1000}},
+            {
+                "predictions": [],
+                "image": {"width": 1000, "height": 1000}
+            },
             ["person", "car", "truck"],
-            (np.empty((0, 4)), np.empty(0), np.empty(0), None),
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None
+            ),
             DoesNotRaise(),
         ),  # empty result
         (
@@ -286,7 +298,156 @@ def test_filter_polygons_by_area(
                 None,
             ),
             DoesNotRaise(),
-        ),  # single bounding box
+        ),  # single correct object detection result
+(
+            {
+                "predictions": [
+                    {
+                        "x": 200.0,
+                        "y": 300.0,
+                        "width": 50.0,
+                        "height": 50.0,
+                        "confidence": 0.9,
+                        "class": "person",
+                    },
+                    {
+                        "x": 500.0,
+                        "y": 500.0,
+                        "width": 100.0,
+                        "height": 100.0,
+                        "confidence": 0.8,
+                        "class": "truck",
+                    }
+                ],
+                "image": {"width": 1000, "height": 1000},
+            },
+            ["person", "car", "truck"],
+            (
+                np.array([[175.0, 275.0, 225.0, 325.0], [450.0, 450.0, 550.0, 550.0]]),
+                np.array([0.9, 0.8]),
+                np.array([0, 2]),
+                None,
+            ),
+            DoesNotRaise(),
+        ),  # two correct object detection result
+        (
+            {
+                "predictions": [
+                    {
+                        "x": 200.0,
+                        "y": 300.0,
+                        "width": 50.0,
+                        "height": 50.0,
+                        "confidence": 0.9,
+                        "class": "person",
+                        "points": []
+                    }
+                ],
+                "image": {"width": 1000, "height": 1000},
+            },
+            ["person", "car", "truck"],
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None
+            ),
+            DoesNotRaise(),
+        ),  # single incorrect instance segmentation result with no points
+        (
+            {
+                "predictions": [
+                    {
+                        "x": 200.0,
+                        "y": 300.0,
+                        "width": 50.0,
+                        "height": 50.0,
+                        "confidence": 0.9,
+                        "class": "person",
+                        "points": [
+                            {"x": 200.0, "y": 300.0},
+                            {"x": 250.0, "y": 300.0}
+                        ]
+                    }
+                ],
+                "image": {"width": 1000, "height": 1000},
+            },
+            ["person", "car", "truck"],
+            (
+                np.empty((0, 4)),
+                np.empty(0),
+                np.empty(0),
+                None
+            ),
+            DoesNotRaise(),
+        ),  # single incorrect instance segmentation result with no enough points
+        (
+                {
+                    "predictions": [
+                        {
+                            "x": 200.0,
+                            "y": 300.0,
+                            "width": 50.0,
+                            "height": 50.0,
+                            "confidence": 0.9,
+                            "class": "person",
+                            "points": [
+                                {"x": 200.0, "y": 300.0},
+                                {"x": 250.0, "y": 300.0},
+                                {"x": 250.0, "y": 350.0},
+                                {"x": 200.0, "y": 350.0},
+                            ]
+                        }
+                    ],
+                    "image": {"width": 1000, "height": 1000},
+                },
+                ["person", "car", "truck"],
+                (
+                    np.array([[175.0, 275.0, 225.0, 325.0]]),
+                    np.array([0.9]),
+                    np.array([0]),
+                    TEST_MASK
+                ),
+                DoesNotRaise(),
+        ),  # single incorrect instance segmentation result with no enough points
+        (
+                {
+                    "predictions": [
+                        {
+                            "x": 200.0,
+                            "y": 300.0,
+                            "width": 50.0,
+                            "height": 50.0,
+                            "confidence": 0.9,
+                            "class": "person",
+                            "points": [
+                                {"x": 200.0, "y": 300.0},
+                                {"x": 250.0, "y": 300.0},
+                                {"x": 250.0, "y": 350.0},
+                                {"x": 200.0, "y": 350.0},
+                            ]
+                        },
+                        {
+                            "x": 500.0,
+                            "y": 500.0,
+                            "width": 100.0,
+                            "height": 100.0,
+                            "confidence": 0.8,
+                            "class": "truck",
+                            "points": []
+                        }
+                    ],
+                    "image": {"width": 1000, "height": 1000},
+                },
+                ["person", "car", "truck"],
+                (
+                    np.array([[175.0, 275.0, 225.0, 325.0]]),
+                    np.array([0.9]),
+                    np.array([0]),
+                    TEST_MASK
+                ),
+                DoesNotRaise(),
+        ),  # two instance segmentation results - one correct, one incorrect
     ],
 )
 def test_process_roboflow_result(