diff --git a/src/perception/camera_object_detection/camera_object_detection/post_synchronizer.py b/src/perception/camera_object_detection/camera_object_detection/post_synchronizer.py
index e2539542..46d5ac62 100644
--- a/src/perception/camera_object_detection/camera_object_detection/post_synchronizer.py
+++ b/src/perception/camera_object_detection/camera_object_detection/post_synchronizer.py
@@ -1,31 +1,85 @@
 import rclpy
 from rclpy.node import Node
 from message_filters import Subscriber, ApproximateTimeSynchronizer
+
+from sensor_msgs.msg import Image
 from vision_msgs.msg import Detection2DArray, Detection2D
 from std_msgs.msg import Header
+
+from ultralytics.utils.plotting import Annotator, colors
+
+from cv_bridge import CvBridgeError
  
-class CameraSyncNode(Node):
+class CameraSyncNode(Node): # synchronizes visualizations
     def __init__(self):
         super().__init__('camera_sync_node')
+
+        self.camera_img_sub = Subscriber(self, Image , '/camera/right/image_color')
  
         self.camera1_sub = Subscriber(self, Detection2DArray, '/camera/left/camera_detections')
         self.camera2_sub = Subscriber(self, Detection2DArray, '/camera/center/camera_detections')
         self.camera3_sub = Subscriber(self, Detection2DArray, '/camera/right/camera_detections')
  
         self.ts = ApproximateTimeSynchronizer(
-            [self.camera1_sub, self.camera2_sub, self.camera3_sub],
+            [self.camera_img_sub, self.camera1_sub, self.camera2_sub, self.camera3_sub],
             queue_size=10,
             slop=0.1)
  
         self.ts.registerCallback(self.callback)
  
         self.combined_detection_publisher = self.create_publisher(Detection2DArray, '/combined_detections', 10)
+        self.vis_publisher = self.create_publisher(Image, '/annotated_img')
+
+    def process_img(self, image):
+        try:
+            cv_image = self.cv_bridge.imgmsg_to_cv2(image, desired_encoding="passthrough")
+        except CvBridgeError as e:
+            self.get_logger().error(str(e))
+            return
+        return cv_image
+    
+    def postprocess_detections(self, detections, annotator):
+        """
+        Post-process draws bouningboxes on camera image.
+
+        Parameters:
+            detections: A list of dict with the format
+                {
+                    "label": str,
+                    "bbox": [float],
+                    "conf": float
+                }
+            annotator: A ultralytics.yolo.utils.plotting.Annotator for the current image
+
+        Returns:
+            processed_detections: filtered detections
+            annotator_img: image with bounding boxes drawn on
+        """
+        processed_detections = detections
+
+        for det in detections:
+            label = f'{det["label"]} {det["conf"]:.2f}'
+            x1, y1, w1, h1 = det["bbox"]
+            xyxy = [x1, y1, x1 + w1, y1 + h1]
+            annotator.box_label(xyxy, label, color=colors(1, True))
+
+        annotator_img = annotator.result()
+        return (processed_detections, annotator_img)
+    
+    def publish_vis(self, annotated_img, msg):
+        # Publish visualizations
+        imgmsg = self.cv_bridge.cv2_to_imgmsg(annotated_img, "bgr8")
+        imgmsg.header.stamp = msg.header.stamp
+        imgmsg.header.frame_id = msg.header.frame_id
+        self.vis_publisher.publish(imgmsg)
  
-    def callback(self, camera1_msg, camera2_msg, camera3_msg):
+    def callback(self, camera_img_sub, camera1_msg, camera2_msg, camera3_msg):
         combined_detections = Detection2DArray()
         combined_detections.header = Header()
         combined_detections.header.stamp = self.get_clock().now().to_msg()
         combined_detections.header.frame_id = camera1_msg.header.frame_id
+
+        cv_image = self.process_img(camera_img_sub)
  
         for detection in camera1_msg.detections:
             combined_detections.detections.append(detection)
@@ -35,8 +89,16 @@ def callback(self, camera1_msg, camera2_msg, camera3_msg):
  
         for detection in camera3_msg.detections:
             combined_detections.detections.append(detection)
+
+        annotator = Annotator(
+            cv_image,
+            line_width=self.line_thickness,
+            example=str(self.names),
+        )
+        (combined_detections, annotated_img) = self.postprocess_detections(combined_detections, annotator)
  
         self.combined_detection_publisher.publish(combined_detections)
+        self.publish_vis(annotated_img, camera_img_sub)
  
 def main(args=None):
     rclpy.init(args=args)
diff --git a/src/perception/camera_object_detection/camera_object_detection/yolov8_detection.py b/src/perception/camera_object_detection/camera_object_detection/yolov8_detection.py
index 05ceb431..c01e408e 100755
--- a/src/perception/camera_object_detection/camera_object_detection/yolov8_detection.py
+++ b/src/perception/camera_object_detection/camera_object_detection/yolov8_detection.py
@@ -281,16 +281,16 @@ def image_callback(self, msg):
                         )
                         self.get_logger().debug(f"{label}: {bbox}")
 
-            annotator = Annotator(
-                cv_image,
-                line_width=self.line_thickness,
-                example=str(self.names),
-            )
-            (detections, annotated_img) = self.postprocess_detections(detections, annotator)
+            # annotator = Annotator(
+            #     cv_image,
+            #     line_width=self.line_thickness,
+            #     example=str(self.names),
+            # )
+            # (detections, annotated_img) = self.postprocess_detections(detections, annotator)
 
             # Currently we support a single camera so we pass an empty string
             feed = ""
-            self.publish_vis(annotated_img, msg, feed)
+            # self.publish_vis(annotated_img, msg, feed)
             self.publish_detections(detections, msg, feed)
 
             if self.save_detections: