diff --git a/frigate/detectors/plugins/openvino.py b/frigate/detectors/plugins/openvino.py
index 5cb1ea39c..ad2bb6f76 100644
--- a/frigate/detectors/plugins/openvino.py
+++ b/frigate/detectors/plugins/openvino.py
@@ -7,6 +7,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.detectors.util import preprocess, yolov8_postprocess
 
 logger = logging.getLogger(__name__)
 
@@ -33,7 +34,9 @@ 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 +83,11 @@ class OvDetector(DetectionApi):
         ]
 
     def detect_raw(self, tensor_input):
+        tensor_input = 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])
 
@@ -132,28 +140,8 @@ class OvDetector(DetectionApi):
                 )
             return detections
         elif self.ov_model_type == ModelTypeEnum.yolov8:
-            out_tensor = infer_request.get_output_tensor()
-            results = out_tensor.data[0]
-            output_data = np.transpose(results)
-            scores = np.max(output_data[:, 4:], axis=1)
-            if len(scores) == 0:
-                return np.zeros((20, 6), np.float32)
-            scores = np.expand_dims(scores, axis=1)
-            # add scores to the last column
-            dets = np.concatenate((output_data, scores), axis=1)
-            # filter out lines with scores below threshold
-            dets = dets[dets[:, -1] > 0.5, :]
-            # limit to top 20 scores, descending order
-            ordered = dets[dets[:, -1].argsort()[::-1]][:20]
-            detections = np.zeros((20, 6), np.float32)
-
-            for i, object_detected in enumerate(ordered):
-                detections[i] = self.process_yolo(
-                    np.argmax(object_detected[4:-1]),
-                    object_detected[-1],
-                    object_detected[:4],
-                )
-            return detections
+            out_tensor = infer_request.get_output_tensor().data
+            return yolov8_postprocess(self.interpreter.inputs[0].shape, out_tensor)
         elif self.ov_model_type == ModelTypeEnum.yolov5:
             out_tensor = infer_request.get_output_tensor()
             output_data = out_tensor.data[0]