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Improved object detection method for autonomous driving based on DETR.

Huaqi Zhao¹, Songnan Zhang¹, Xiang Peng¹

¹The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence and Information Processing, School of Information and Electronic Technology, Jiamusi University, Jiamusi, China.

Frontiers in Neurorobotics

|February 4, 2025

Summary

This summary is machine-generated.

This study introduces an enhanced object detection method for autonomous driving using a detection transformer (DETR). The improved model achieves higher accuracy and faster inference speeds, crucial for safe self-driving technology.

Keywords:

feature extraction loss function object detection parameter tuning transformer encoder

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Object detection is vital for autonomous driving systems.
Current methods face limitations in multi-scale object localization and detection efficiency.
Detection Transformer (DETR) models offer a promising foundation for advanced object detection.

Purpose of the Study:

To improve object detection accuracy and inference speed for autonomous driving.
To address the limitations of existing multi-scale object detection techniques.
To develop a more efficient and accurate DETR-based object detection system.

Main Methods:

Introduced a multi-scale feature and location information extraction method.
Developed a transformer encoder utilizing a group axial attention mechanism for efficient computation and attention control.
Implemented a dynamic hyperparameter tuning training method based on Pareto efficiency for optimized loss function coordination.

Main Results:

Achieved significant improvements in average precision across multiple benchmark datasets: 3.3% on COCO, 4.5% on PASCAL VOC, and 3% on KITTI.
Demonstrated an 84% increase in frames per second (FPS), indicating enhanced inference speed.
The dynamic hyperparameter tuning method improved model convergence speed and overall accuracy.

Conclusions:

The proposed DETR-based object detection method offers superior performance for autonomous driving applications.
The integration of multi-scale feature extraction, group axial attention, and dynamic hyperparameter tuning leads to substantial gains in accuracy and efficiency.
This research contributes to the advancement of robust and high-performing object detection systems for autonomous vehicles.