Incorrect ROS Package Configuration

Oct 13, 2025 by talynt.org 146 views

In a Custom Dataset, Certain Categories Cannot be Detected: Troubleshooting Object Tracking with YOLOv8/YOLOv12 in ROS

Designing a strawberry-picking robot is an exciting project that requires the integration of various technologies, including computer vision and robotics. In this context, object tracking is a crucial component that enables the robot to detect and follow the strawberries. However, when working with a custom dataset, certain categories may not be detected properly, which can lead to inaccurate tracking and reduced performance. In this article, we will explore the possible causes of this issue and provide guidance on how to troubleshoot the problem.

The issue at hand is that certain categories are not detected properly when running object tracking with YOLOv8/YOLOv12 in a ROS package. This is in contrast to the results obtained when running direct prediction using Python and YOLO on a PC, where the performance is correct. The problem arises when the trained weights are imported into the ROS2 package and inference is run.

To troubleshoot the issue, let's analyze the problem step by step.

1. Dataset Verification

The first step is to verify that the dataset is correct. This involves checking the image quality, labeling, and formatting to ensure that they meet the requirements of the object tracking algorithm.

2. ROS Package Configuration

The next step is to review the ROS package configuration to ensure that it is set up correctly. This includes checking the input image topic, model, and other parameters to ensure that they are consistent with the requirements of the object tracking algorithm.

3. Inference Performance

The third step is to investigate the inference performance of the ROS package. This involves checking the processing time, memory usage, and other metrics to identify any potential bottlenecks or issues that may be contributing to the problem.

Based on the analysis, there are several possible causes of the issue:

1. Inconsistent Dataset

The dataset may not be consistent with the requirements of the object tracking algorithm. This could be due to issues with the image quality, labeling, or formatting.

2. Incorrect ROS Package Configuration

The ROS package configuration may not be set up correctly, leading to inconsistent results.

3. Inference Performance Issues

The inference performance of the ROS package may be affected by issues such as processing time, memory usage, or other bottlenecks.

4. Model Incompatibility

The model used in the ROS package may not be compatible with the object tracking algorithm, leading to inconsistent results.

To troubleshoot the issue, follow these steps:

1. Verify Dataset

Verify that the dataset is correct and consistent with the requirements of the object tracking algorithm.

2. Review ROS Package Configuration

Review the ROS package configuration to ensure that it is set up correctly.

3. Investigate Inference Performance

Investigate the inference performance of the ROS package to identify any potential bottlenecks or issues.

4. Check Model Compatibility

Check the model used in the ROS package to ensure that it is compatible with the object tracking algorithm.

In conclusion, the issue of certain categories not being detected properly in a custom dataset can be caused by a variety of factors, including inconsistent dataset, incorrect ROS package configuration, inference performance issues, and model incompatibility. By following the troubleshooting steps outlined in this article, you can identify and resolve the issue, ensuring accurate object tracking and improved performance of your strawberry-picking robot.

Based on the analysis and troubleshooting steps outlined in this article, the following recommendations are made:

1. Verify Dataset

Verify that the dataset is correct and consistent with the requirements of the object tracking algorithm.

2. Review ROS Package Configuration

Review the ROS package configuration to ensure that it is set up correctly.

3. Investigate Inference Performance

Investigate the inference performance of the ROS package to identify any potential bottlenecks or issues.

4. Check Model Compatibility

Check the model used in the ROS package to ensure that it is compatible with the object tracking algorithm.

Future work on this project includes:

1. Improving Dataset Quality

Improve the quality of the dataset by collecting more images and labeling them accurately.

2. Optimizing ROS Package Configuration

Optimize the ROS package configuration to improve inference performance and reduce processing time.

3. Developing New Models

Develop new models that are compatible with the object tracking algorithm and improve performance.

By following these recommendations and future work, you can improve the accuracy and performance of your strawberry-picking robot and ensure successful deployment in the field.
Q&A: Troubleshooting Object Tracking with YOLOv8/YOLOv12 in ROS

In our previous article, we explored the issue of certain categories not being detected properly in a custom dataset when running object tracking with YOLOv8/YOLOv12 in a ROS package. We also provided guidance on how to troubleshoot the problem. In this article, we will answer some frequently asked questions (FAQs) related to this issue.

A: The common causes of the issue include:

Inconsistent dataset
Incorrect ROS package configuration
Inference performance issues
Model incompatibility

A: To verify that your dataset is correct, you can:

Check the image quality and labeling
Ensure that the dataset is consistent with the requirements of the object tracking algorithm
Use tools such as OpenCV to visualize and inspect the dataset

A: The key parameters to check in the ROS package configuration include:

Input image topic
Model
Use 3D
Use tracking
Image size

A: To investigate inference performance issues, you can:

Use tools such as ROS's built-in profiling tools to measure processing time and memory usage
Use tools such as OpenCV to visualize and inspect the inference output
Use tools such as TensorFlow to measure inference performance

A: To check model compatibility, you can:

Check the model architecture and weights
Ensure that the model is compatible with the object tracking algorithm
Use tools such as TensorFlow to measure model performance

A: The best practices for troubleshooting object tracking issues include:

Verifying the dataset and ROS package configuration
Investigating inference performance issues
Checking model compatibility
Using tools such as OpenCV and TensorFlow to visualize and inspect the inference output

A: To improve the accuracy and performance of your strawberry-picking robot, you can:

Improve the quality of the dataset
Optimize the ROS package configuration
Develop new models that are compatible with the object tracking algorithm
Use tools such as OpenCV and TensorFlow to visualize and inspect the inference output

In conclusion, troubleshooting object tracking issues with YOLOv8/YOLOv12 in ROS requires a systematic approach that involves verifying the dataset and ROS package configuration, investigating inference performance issues, and checking model compatibility. By following the best practices outlined in this article, you can improve the accuracy and performance of your strawberry-picking robot and ensure successful deployment in the field.

Based on the FAQs and troubleshooting steps outlined in this article, the following recommendations are made:

Verify the dataset and ROS package configuration
Investigate inference performance issues
Check model compatibility
Use tools such as OpenCV and TensorFlow to visualize and inspect the inference output

Future work on this project includes:

Improving the quality of the dataset
Optimizing the ROS package configuration
Developing new models that are compatible with the object tracking algorithm
Using tools such as OpenCV and TensorFlow to visualize and inspect the inference output

By following these recommendations and future work, you can improve the accuracy and performance of your strawberry-picking robot and ensure successful deployment in the field.