diff --git a/frigate/detectors/plugins/onnx.py b/frigate/detectors/plugins/onnx.py
index c98665d4d..428b68078 100644
--- a/frigate/detectors/plugins/onnx.py
+++ b/frigate/detectors/plugins/onnx.py
@@ -51,7 +51,7 @@ class ONNXDetector(DetectionApi):
         model_input_name = self.model.get_inputs()[0].name
         model_input_shape = self.model.get_inputs()[0].shape
 
-        tensor_input = yolo_utils.yolov8_preprocess(tensor_input, model_input_shape)
+        tensor_input = yolo_utils.preprocess(tensor_input, model_input_shape, np.float32)
 
         tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
 
diff --git a/frigate/detectors/plugins/openvino.py b/frigate/detectors/plugins/openvino.py
index 5cb1ea39c..5d748f1e7 100644
--- a/frigate/detectors/plugins/openvino.py
+++ b/frigate/detectors/plugins/openvino.py
@@ -8,6 +8,8 @@ from typing_extensions import Literal
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 
+import frigate.detectors.yolo_utils as yolo_utils
+
 logger = logging.getLogger(__name__)
 
 DETECTOR_KEY = "openvino"
@@ -33,7 +35,7 @@ class OvDetector(DetectionApi):
             model=self.ov_model, device_name=detector_config.device
         )
 
-        logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
+        logger.info(f"Model Input Shape: {self.interpreter.input(0).shape} {self.interpreter.input(0).element_type.to_dtype()}")
         self.output_indexes = 0
 
         while True:
@@ -80,6 +82,7 @@ class OvDetector(DetectionApi):
         ]
 
     def detect_raw(self, tensor_input):
+        tensor_input = yolo_utils.preprocess(tensor_input, self.interpreter.inputs[0].shape, self.interpreter.inputs[0].element_type.to_dtype())
         infer_request = self.interpreter.create_infer_request()
         infer_request.infer([tensor_input])
 
diff --git a/frigate/detectors/plugins/rocm.py b/frigate/detectors/plugins/rocm.py
index 6f2c3c1a8..d5d0ba585 100644
--- a/frigate/detectors/plugins/rocm.py
+++ b/frigate/detectors/plugins/rocm.py
@@ -102,7 +102,7 @@ class ROCmDetector(DetectionApi):
         model_input_name = self.model.get_parameter_names()[0];
         model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens());
 
-        tensor_input = yolo_utils.yolov8_preprocess(tensor_input, model_input_shape)
+        tensor_input = yolo_utils.preprocess(tensor_input, model_input_shape, np.float32)
 
         detector_result = self.model.run({model_input_name: tensor_input})[0]
 
diff --git a/frigate/detectors/yolo_utils.py b/frigate/detectors/yolo_utils.py
index b53f85a9d..02442ab65 100644
--- a/frigate/detectors/yolo_utils.py
+++ b/frigate/detectors/yolo_utils.py
@@ -5,11 +5,20 @@ import cv2
 
 logger = logging.getLogger(__name__)
 
-def yolov8_preprocess(tensor_input, model_input_shape):
+def preprocess(tensor_input, model_input_shape, model_input_element_type):
+    model_input_shape = tuple(model_input_shape)
+    assert tensor_input.dtype == np.uint8, f'tensor_input.dtype: {tensor_input.dtype}'
+    if len(tensor_input.shape) == 3:
+        tensor_input = tensor_input[np.newaxis, :]
+    if model_input_element_type == np.uint8:
+        # nothing to do for uint8 model input
+        assert model_input_shape == tensor_input.shape, f'model_input_shape: {model_input_shape}, tensor_input.shape: {tensor_input.shape}'
+        return tensor_input
+    assert model_input_element_type == np.float32, f'model_input_element_type: {model_input_element_type}'
     # tensor_input must be nhwc
-    assert tensor_input.shape[3] == 3
-    if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
-        logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
+    assert tensor_input.shape[3] == 3, f'tensor_input.shape: {tensor_input.shape}'
+    if tensor_input.shape[1:3] != model_input_shape[2:4]:
+        logger.warn(f"preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
     # cv2.dnn.blobFromImage is faster than numpying it
     return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)