diff --git a/frigate/detectors/yolo_utils.py b/frigate/detectors/yolo_utils.py
index 79586890e..b53f85a9d 100644
--- a/frigate/detectors/yolo_utils.py
+++ b/frigate/detectors/yolo_utils.py
@@ -13,20 +13,31 @@ def yolov8_preprocess(tensor_input, model_input_shape):
     # 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)
 
-def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20):
+def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20, score_threshold = 0.3, nms_threshold = 0.5):
     model_box_count = tensor_output.shape[2]
     probs = tensor_output[0, 4:, :]
     all_ids = np.argmax(probs, axis=0)
     all_confidences = probs.T[np.arange(model_box_count), all_ids]
     all_boxes = tensor_output[0, 0:4, :].T
-    mask = (all_confidences > 0.30)
+    mask = (all_confidences > score_threshold)
     class_ids = all_ids[mask]
     confidences = all_confidences[mask]
     cx, cy, w, h = all_boxes[mask].T
-    scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
+
+    if model_input_shape[3] == 3:
+        scale_y, scale_x = 1 / model_input_shape[1], 1 / model_input_shape[2]
+    else:
+        scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
     detections = np.stack((class_ids, confidences, scale_y * (cy - h / 2), scale_x * (cx - w / 2), scale_y * (cy + h / 2), scale_x * (cx + w / 2)), axis=1)
     if detections.shape[0] > box_count:
-        detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
+        # if too many detections, do nms filtering to suppress overlapping boxes
+        boxes = np.stack((cx - w / 2, cy - h / 2, w, h), axis=1)
+        indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold, nms_threshold)
+        detections = detections[indexes]
+        # if still too many, trim the rest by confidence
+        if detections.shape[0] > box_count:
+            detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
+        detections = detections.copy()
     detections.resize((box_count, 6))
     return detections