Object detection pipeline in ROS - Step-by-Step Execution

Concept Flow - Object detection pipeline in ROS

Start ROS Node

↓

Subscribe to Camera Topic

↓

Receive Image Message

↓

Preprocess Image

↓

Run Object Detection Model

↓

Publish Detection Results

↓

Visualize or Use Results

↓

Wait for Next Image

The pipeline starts by running a ROS node that subscribes to camera images, processes each image to detect objects, publishes the detection results, and then waits for the next image.

Execution Sample

ROS

def image_callback(msg):
    img = bridge.imgmsg_to_cv2(msg)
    detections = model.detect(img)
    pub.publish(detections)

rospy.Subscriber('/camera/image_raw', Image, image_callback)
rospy.spin()

This code subscribes to camera images, converts them, runs detection, publishes results, and keeps running.

Execution Table

Step	Action	Input	Output	Next Step
1	Start ROS Node	None	Node initialized	Subscribe to camera topic
2	Subscribe to /camera/image_raw	None	Listening for images	Receive image message
3	Receive Image Message	Raw image message	Image data in callback	Preprocess image
4	Preprocess Image	Raw image data	CV image format	Run object detection
5	Run Object Detection Model	CV image	List of detected objects	Publish detection results
6	Publish Detection Results	Detections list	Published to topic	Visualize or use results
7	Visualize or Use Results	Published detections	Displayed or used by other nodes	Wait for next image
8	Wait for Next Image	None	Idle until new image	Receive image message
9	Repeat steps 3-8	New image message	New detections	Loop continues

💡 The node runs continuously until manually stopped, processing each incoming image.

Variable Tracker

Variable	Start	After 1 Image	After 2 Images	After 3 Images	Final
msg	None	Image1 message	Image2 message	Image3 message	Latest image message
img	None	CV image1	CV image2	CV image3	Latest CV image
detections	None	[obj1, obj2]	[obj3]	[obj1, obj4]	Latest detections list

Key Moments - 3 Insights

Why does the callback function run multiple times?

Because the callback is triggered each time a new image message arrives, as shown in execution_table rows 3, 8, and 9.

What happens if the detection model takes too long?

The node will delay processing new images, causing a backlog. This is implied by the wait step in row 8 waiting for the next image.

Why do we convert the image message before detection?

Because the detection model needs a CV image format, not the raw ROS message, as shown in step 4 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output of step 5?

A List of detected objects

B Raw image message

C Published detection topic

D CV image format

💡 Hint

Check the 'Output' column for step 5 in the execution_table.

At which step does the node wait for the next image?

A Step 3

B Step 8

C Step 6

D Step 1

💡 Hint

Look for the step labeled 'Wait for Next Image' in the execution_table.

If the image_callback function is not called, which variable stays None according to variable_tracker?

A detections

B img

C msg

D pub

💡 Hint

Check the variable 'msg' in variable_tracker; it changes only when callback runs.

Concept Snapshot

ROS Object Detection Pipeline:
- Start node and subscribe to camera topic
- Receive image messages in callback
- Convert images to CV format
- Run detection model on images
- Publish detection results
- Repeat for each new image

Full Transcript

This visual execution shows how an object detection pipeline works in ROS. The node starts and subscribes to a camera image topic. Each time an image arrives, the callback runs: it converts the image, runs the detection model, and publishes the results. The node then waits for the next image and repeats. Variables like the image message, converted image, and detections update each cycle. Key moments include understanding why the callback runs repeatedly, the need for image conversion, and the waiting step. The quiz questions help check understanding of outputs and flow steps.

Practice

1. What is the main purpose of an object detection pipeline in ROS?

easy

A. To help robots find and recognize objects around them

B. To control the robot's movement speed

C. To manage battery usage efficiently

D. To create 3D maps of the environment

Solution

1. Step 1: Understand the role of object detection in robotics

   Object detection helps robots identify and locate objects in their surroundings.

2. Step 2: Differentiate from other robot functions

   Controlling speed, battery, or mapping are different tasks not directly related to object detection.

3. Final Answer:

   To help robots find and recognize objects around them -> Option A

4. Quick Check:

   Object detection = finding objects [OK]

Hint: Object detection means finding objects, not moving or mapping [OK]

Common Mistakes:

• Confusing object detection with robot movement control
• Thinking object detection manages battery
• Mixing object detection with mapping tasks

2. Which ROS command is used to start an object detection pipeline defined in a launch file?

easy

A. rosrun object_detection detect_node

B. rosstart object_detection pipeline

C. roslaunch object_detection pipeline.launch

D. rosinit object_detection start

Solution

1. Step 1: Identify the command to start multiple ROS nodes together

   roslaunch is used to start nodes defined in a launch file.

2. Step 2: Check the options for correct syntax

   roslaunch object_detection pipeline.launch matches the correct syntax to launch the pipeline.

3. Final Answer:

   roslaunch object_detection pipeline.launch -> Option C

4. Quick Check:

   Use roslaunch to start launch files [OK]

Hint: Use roslaunch for launch files, rosrun for single nodes [OK]

Common Mistakes:

• Using rosrun instead of roslaunch for launch files
• Using non-existent commands like rosstart or rosinit
• Missing the .launch extension

3. Given this ROS Python snippet for object detection node:

import rospy
from sensor_msgs.msg import Image

def callback(data):
    rospy.loginfo('Image received')

rospy.init_node('detector')
rospy.Subscriber('/camera/image_raw', Image, callback)
rospy.spin()

What will be printed when an image message arrives on /camera/image_raw?

medium

A. Image processed successfully

B. No output, node does not subscribe

C. Error: callback not defined

D. Image received

Solution

1. Step 1: Understand the subscriber and callback

   The node subscribes to '/camera/image_raw' and calls callback on message arrival.

2. Step 2: Check what callback does

   Callback prints 'Image received' using rospy.loginfo when called.

3. Final Answer:

   Image received -> Option D

4. Quick Check:

   Callback prints 'Image received' on message [OK]

Hint: Subscriber calls callback printing 'Image received' [OK]

Common Mistakes:

• Thinking no output occurs without explicit print
• Confusing rospy.loginfo with error
• Assuming callback is undefined

4. You wrote this launch file snippet to start your object detection node:

<launch>
  <node pkg="object_detection" type="detector.py" name="detector" />
</launch>

But when you run roslaunch, you get an error saying the node type is not found. What is the likely problem?

medium

A. The node file 'detector.py' is not marked executable

B. The package name 'object_detection' is incorrect

C. The launch file syntax is invalid

D. The node name 'detector' is already in use

Solution

1. Step 1: Check common causes for 'type not found' error

   This error often happens if the node script is not executable.

2. Step 2: Verify other options

   Package name or syntax errors cause different errors; node name conflict causes runtime warnings.

3. Final Answer:

   The node file 'detector.py' is not marked executable -> Option A

4. Quick Check:

   Make node scripts executable to avoid 'type not found' [OK]

Hint: Check file permissions if node type not found error appears [OK]

Common Mistakes:

• Assuming package name is wrong without checking
• Thinking launch syntax is always the cause
• Ignoring file permission issues

5. You want to improve your ROS object detection pipeline by adding a node that filters detected objects by size before publishing results. Which approach best fits this requirement?

hard

A. Publish filtered results directly from the detection node without a new node

B. Add a new node subscribing to detection results, filter objects, then publish filtered results

C. Change the launch file to exclude the detection node and only run the filter node

D. Modify the camera driver node to filter objects before detection

Solution

1. Step 1: Understand modular ROS pipeline design

   Adding a separate node to filter results keeps pipeline modular and clear.

2. Step 2: Evaluate other options

   Modifying camera driver or excluding detection breaks pipeline; filtering inside detection node reduces modularity.

3. Final Answer:

   Add a new node subscribing to detection results, filter objects, then publish filtered results -> Option B

4. Quick Check:

   Use separate nodes for filtering to keep pipeline modular [OK]

Hint: Add filter as separate node subscribing to detection output [OK]

Common Mistakes:

• Modifying unrelated nodes like camera driver
• Removing detection node from launch file
• Mixing filtering inside detection node reducing clarity