diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index 9e7a30d0e..49af70078 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -366,29 +366,30 @@ def _average_precisions_per_class(
 
     def _detections_content(self, detections: Detections) -> np.ndarray:
         """Return boxes, masks or oriented bounding boxes from detections."""
-        if self._metric_target == MetricTarget.BOXES:
+        mt = self._metric_target  # cache for speed
+        if mt is MetricTarget.BOXES:
             return detections.xyxy
-        if self._metric_target == MetricTarget.MASKS:
+        elif mt is MetricTarget.MASKS:
+            mask_content = detections.mask
             return (
-                detections.mask
-                if detections.mask is not None
-                else self._make_empty_content()
+                mask_content if mask_content is not None else self._make_empty_content()
             )
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+        elif mt is MetricTarget.ORIENTED_BOUNDING_BOXES:
             obb = detections.data.get(ORIENTED_BOX_COORDINATES)
-            if obb is not None and len(obb) > 0:
-                return np.array(obb, dtype=np.float32)
+            if obb:  # check if obb is not None and non-empty
+                return np.asarray(obb, dtype=np.float32)
             return self._make_empty_content()
-        raise ValueError(f"Invalid metric target: {self._metric_target}")
+        raise ValueError(f"Invalid metric target: {mt}")
 
     def _make_empty_content(self) -> np.ndarray:
-        if self._metric_target == MetricTarget.BOXES:
+        mt = self._metric_target  # use local cached attribute
+        if mt is MetricTarget.BOXES:
             return np.empty((0, 4), dtype=np.float32)
-        if self._metric_target == MetricTarget.MASKS:
+        elif mt is MetricTarget.MASKS:
             return np.empty((0, 0, 0), dtype=bool)
-        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
+        elif mt is MetricTarget.ORIENTED_BOUNDING_BOXES:
             return np.empty((0, 4, 2), dtype=np.float32)
-        raise ValueError(f"Invalid metric target: {self._metric_target}")
+        raise ValueError(f"Invalid metric target: {mt}")
 
     def _filter_detections_by_size(
         self, detections: Detections, size_category: ObjectSizeCategory