diff --git a/src/dopt_sensor_anomalies/detection.py b/src/dopt_sensor_anomalies/detection.py
index 57fbcce..de9cf04 100644
--- a/src/dopt_sensor_anomalies/detection.py
+++ b/src/dopt_sensor_anomalies/detection.py
@@ -31,14 +31,17 @@ pixels_per_metric_Y: float = 0.251
 
 
 # measuring
-def midpoint(pt_A: npt.NDArray, pt_B: npt.NDArray) -> tuple[float, float]:
+def midpoint(
+    pt_A: npt.NDArray[np.floating],
+    pt_B: npt.NDArray[np.floating],
+) -> tuple[float, float]:
     """to identify the midpoint of a 2D area
 
     Parameters
     ----------
-    pt_A : npt.NDArray
+    pt_A : npt.NDArray[np.floating]
         tuple of coordinates x, y; shape (2, )
-    pt_B : npt.NDArray
+    pt_B : npt.NDArray[np.floating]
         tuple of coordinates x, y; shape (2, )
 
     Returns
@@ -50,17 +53,17 @@ def midpoint(pt_A: npt.NDArray, pt_B: npt.NDArray) -> tuple[float, float]:
 
 
 def check_box_redundancy(
-    box1: t.Box,
-    box2: t.Box,
+    box_1: t.Box,
+    box_2: t.Box,
     tolerance: float = 5.0,
 ) -> bool:
     """to check if bounding box has already been identified and is just a redundant one
 
     Parameters
     ----------
-    box1 : t.Box
+    box_1 : t.Box
         tuple of box values: ((center_x, center_y), (width, height), angle)
-    box2 : t.Box
+    box_2 : t.Box
         tuple of box values: ((center_x, center_y), (width, height), angle)
     tolerance : float, optional
         distance threshold for width and height, by default 5.0
@@ -71,8 +74,8 @@ def check_box_redundancy(
         redundancy evaluation
     """
     # unpack the boxes
-    c1, s1, _ = box1
-    c2, s2, _ = box2
+    c1, s1, _ = box_1
+    c2, s2, _ = box_2
     # sort width and height such that (w, h) == (h, w) is treated the same
     # (might have been recognized in different orders)
     s1 = sorted(s1)
@@ -81,7 +84,7 @@ def check_box_redundancy(
     center_dist = cast(float, np.linalg.norm(np.array(c1) - np.array(c2)))
     size_diff = cast(float, np.linalg.norm(np.array(s1) - np.array(s2)))
 
-    return center_dist < tolerance and size_diff < tolerance
+    return bool(center_dist < tolerance and size_diff < tolerance)
 
 
 # ** main function
@@ -168,14 +171,14 @@ def measure_length(
         box = cv2.boxPoints(rbox)
         box = np.array(box, dtype=np.int32)
         # order the points in the contour in top-left, top-right, bottom-right, and bottom-left
-        box = perspective.order_points(box)
+        box = cast(npt.NDArray[np.float32], perspective.order_points(box))
 
         # unpack the bounding box
         (tl, tr, br, bl) = box
         # compute the midpoints between the top-left and top-right as well as bottom-left and bottom-right coordinates
         (tltrX, tltrY) = midpoint(tl, tr)
         (blbrX, blbrY) = midpoint(bl, br)
-        # compute the midpoints between the top-left and top-right as well as the top-right and bottom-right coordinates
+        # compute the midpoints between the top-left and bottom-left as well as the top-right and bottom-right coordinates
         (tlblX, tlblY) = midpoint(tl, bl)
         (trbrX, trbrY) = midpoint(tr, br)