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Enhancing Deep Learning-Based Segmentation Accuracy through Intensity Rendering and 3D Point Interpolation Techniques

Myeong-Jun Kim¹, Suyeon Kim², Banghyon Lee²

¹Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This summary is machine-generated.

This study introduces new methods for LiDAR sensor data to improve object detection in autonomous vehicles. These techniques enhance performance by reducing discrepancies between training and real-world data, boosting accuracy by approximately 20%.

Keywords:

3D segmentation LiDAR sensor data annotation deep learning intensity rendering object detection

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Area of Science:

Computer Vision
Robotics
Sensor Fusion

Background:

Deep learning segmentation networks are vital for object identification in autonomous vehicles using LiDAR.
Discrepancies in LiDAR sensor data (coordinates, intensity) between training and real-world deployment cause performance degradation.

Purpose of the Study:

To develop and evaluate novel techniques for harmonizing LiDAR sensor data attributes.
To enhance the performance and reliability of deep learning perception systems in autonomous vehicles.
To prevent performance degradation when using diverse LiDAR sensors for training and deployment.

Main Methods:

Proposed novel intensity rendering techniques.
Developed data interpolation methods to reconcile sensor data differences.
Evaluated methods on object tracking in real-world autonomous driving scenarios.

Main Results:

Achieved an approximate 20% improvement in mean Intersection over Union (mIoU) performance.
Demonstrated effective harmonization of sensor data attributes.
Validated the prevention of performance degradation across different sensor types.

Conclusions:

The proposed intensity rendering and data interpolation techniques significantly improve LiDAR-based object detection in autonomous vehicles.
These methods enable the use of diverse, open-source datasets, saving time and resources on data annotation.
The approach enhances the robustness and reliability of autonomous driving perception systems.