diff --git a/src/image_object_detection/image_object_detection_node.py b/src/image_object_detection/image_object_detection_node.py
index 0ec44da..855ddab 100644
--- a/src/image_object_detection/image_object_detection_node.py
+++ b/src/image_object_detection/image_object_detection_node.py
@@ -12,13 +12,13 @@
 import cv2
 
 import cv_bridge
+from cv_bridge import CvBridgeError
 import rclpy
 from rclpy.node import Node
 from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
 
 import std_srvs.srv
 from sensor_msgs.msg import CompressedImage, Image
-from vision_msgs.msg import Detection2D, ObjectHypothesisWithPose
 
 import torch
 import torch.backends.cudnn as cudnn
@@ -27,7 +27,7 @@
 from utils.general import check_img_size, non_max_suppression, scale_coords, xyxy2xywh, set_logging
 from utils.plots import plot_one_box
 from utils.torch_utils import select_device
-from vision_msgs.msg import Detection2DArray, Detection2D
+from vision_msgs.msg import Detection2D, Detection2DArray, ObjectHypothesisWithPose
 from ament_index_python.packages import get_package_share_directory
 
 PACKAGE_NAME = "image_object_detection"
@@ -102,7 +102,7 @@ def __init__(self):
             self.get_parameter("subscribers.qos_policy").get_parameter_value().string_value
         )
 
-        self.debug_image_output_format = "mono8"  # "bgr8"
+        self.debug_image_output_format = "mono8" # "mono8"  # "bgr8"
 
         self.processing_enabled = self.get_parameter_or("processing_enabled", True)
 
@@ -215,13 +215,17 @@ def accomodate_image_to_model(self, img0):
     def image_compressed_callback(self, msg):
         if not self.processing_enabled:
             return
+        
+        try:
+            self.cv_img = self.bridge.compressed_imgmsg_to_cv2(msg, self.debug_image_output_format)
+            img = self.accomodate_image_to_model(self.cv_img)
 
-        self.cv_img = self.bridge.compressed_imgmsg_to_cv2(msg, self.debug_image_output_format)
-        img = self.accomodate_image_to_model(self.cv_img)
-
-        detections_msg, debugimg = self.predict(img, self.cv_img)
+            detections_msg, debugimg = self.predict(img, self.cv_img)
 
-        self.detection_publisher.publish(detections_msg)
+            self.detection_publisher.publish(detections_msg)
+        except CvBridgeError as e:
+            self.get_logger().error(f"Error converting image: {e}")
+            return
 
         if debugimg is not None:
             self.publish_debug_image(debugimg)
@@ -256,7 +260,7 @@ def publish_debug_image(self, debugimg):
         elif self.debug_image_output_format == "rgba8":
             debugimg = cv2.cvtColor(debugimg, cv2.COLOR_BGR2RGBA)
         else:
-            self.logger.error(
+            self.get_logger().error(
                 "Unsupported debug image output format: {}".format(self.debug_image_output_format)
             )
             return
@@ -295,8 +299,8 @@ def predict(self, model_img, original_image):
                             detection2D_msg = Detection2D()
                             xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
 
-                            detection2D_msg.bbox.center.x = xywh[0]
-                            detection2D_msg.bbox.center.y = xywh[1]
+                            detection2D_msg.bbox.center.position.x = xywh[0]
+                            detection2D_msg.bbox.center.position.y = xywh[1]
                             detection2D_msg.bbox.size_x = xywh[2]
                             detection2D_msg.bbox.size_y = xywh[3]