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Lane Detection Based on ECBAM_ASPP Model.

Xiang Gu¹, Qiwei Huang², Chaonan Du¹

¹School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces the ECBAM_ASPP model for faster and more accurate autonomous driving lane detection. The model enhances feature learning and multi-scale extraction for improved real-time performance and safety.

Keywords:

attention mechanism autonomous driving deep learning lane detection

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Systems

Background:

Accurate and efficient lane detection is critical for autonomous driving safety and performance.
Real-time processing demands high detection speed alongside accuracy.
Existing methods face challenges in balancing speed and precision.

Purpose of the Study:

To propose an efficient and accurate lane detection model for autonomous driving.
To enhance feature extraction and multi-scale analysis for improved lane recognition.
To address the need for real-time performance in autonomous vehicle systems.

Main Methods:

Integration of the Efficient Convolutional Block Attention Module (ECBAM) with Atrous Spatial Pyramid Pooling (ASPP).
ECBAM utilizes dynamic convolution kernels for efficient feature channel learning and local interactions.
ASPP enables multi-scale feature extraction through variable input sampling.

Main Results:

The ECBAM_ASPP model significantly improves real-time performance in lane detection.
High detection accuracy is maintained, outperforming baseline methods.
Experimental validation on TuSimple and CULane datasets confirms superior robustness and practicality.

Conclusions:

The proposed ECBAM_ASPP model offers a robust solution for real-time lane detection in autonomous driving.
The integration of ECBAM and ASPP effectively enhances feature focus and multi-scale understanding.
The model demonstrates significant advancements in both speed and accuracy for practical autonomous systems.