diff --git a/docs/detection/utils.md b/docs/detection/utils.md
index 323b2954..95b3d5b7 100644
--- a/docs/detection/utils.md
+++ b/docs/detection/utils.md
@@ -5,6 +5,12 @@ status: new
 
 # Detection Utils
 
+<div class="md-typeset">
+  <h2><a href="#supervision.detection.utils.box_iou">box_iou</a></h2>
+</div>
+
+:::supervision.detection.utils.box_iou
+
 <div class="md-typeset">
   <h2><a href="#supervision.detection.utils.box_iou_batch">box_iou_batch</a></h2>
 </div>
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index a9ee8225..09e4a87a 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -48,32 +48,41 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
     return mask
 
 
-def box_iou(box1: np.ndarray, box2: np.ndarray) -> float:
+def box_iou(box_true: Union[List[float], np.ndarray], box_detection: Union[List[float], np.ndarray]) -> float:
     """
     Compute the Intersection over Union (IoU) between two bounding boxes.
 
+    Both `box_true` and `box_detection` should be in (x_min, y_min, x_max, y_max) format.
+    
+    Note:
+        Use `box_iou` when computing IoU between two individual boxes.
+        For comparing multiple boxes (arrays of boxes), use `box_iou_batch` for better performance.
+    
     Args:
-        box1 (np.ndarray): A bounding box represented as [x1, y1, x2, y2].
-        box2 (np.ndarray): A bounding box represented as [x1, y1, x2, y2].
+        box_true (Union[List[float], np.ndarray]): A single bounding box represented as [x_min, y_min, x_max, y_max].
+        box_detection (Union[List[float], np.ndarray]): A single bounding box represented as [x_min, y_min, x_max, y_max].
 
     Returns:
-        float: The IoU value between box1 and box2.
+        float: The IoU value between the two boxes.
                Ranges from 0.0 (no overlap) to 1.0 (perfect overlap).
     """
-    inter_x1 = max(box1[0], box2[0])
-    inter_y1 = max(box1[1], box2[1])
-    inter_x2 = min(box1[2], box2[2])
-    inter_y2 = min(box1[3], box2[3])
+    box_true = np.array(box_true)
+    box_detection = np.array(box_detection)
+
+    inter_x1 = max(box_true[0], box_detection[0])
+    inter_y1 = max(box_true[1], box_detection[1])
+    inter_x2 = min(box_true[2], box_detection[2])
+    inter_y2 = min(box_true[3], box_detection[3])
 
     inter_w = max(0, inter_x2 - inter_x1)
     inter_h = max(0, inter_y2 - inter_y1)
 
     inter_area = inter_w * inter_h
 
-    area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
-    area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
+    area_true = (box_true[2] - box_true[0]) * (box_true[3] - box_true[1])
+    area_detection = (box_detection[2] - box_detection[0]) * (box_detection[3] - box_detection[1])
 
-    union_area = area1 + area2 - inter_area
+    union_area = area_true + area_detection - inter_area
 
     return inter_area / union_area + 1e-6