Drivable area recognition on unstructured roads for autonomous vehicles using an optimized bilateral neural network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a lightweight semantic segmentation network for accurate drivable area recognition in autonomous vehicles. The model enhances safety on unstructured roads by effectively handling complex boundaries and achieving real-time performance.
Area Of Science
- Computer Vision
- Autonomous Systems
- Deep Learning
Background
- Accurate drivable area recognition is crucial for autonomous vehicle safety, especially on unstructured roads with diverse shapes and ambiguous boundaries.
- Existing models often fail to balance real-time performance with high accuracy on complex road scenarios.
Purpose Of The Study
- To propose a lightweight bilateral semantic segmentation network with a dual attention mechanism for enhanced drivable area recognition.
- To improve the accuracy and real-time capabilities of autonomous driving systems on unstructured roads.
Main Methods
- Developed a lightweight bilateral semantic segmentation network integrating Efficient Channel Attention (ECA) and Coordinate Attention (CA) mechanisms within the BiSeNet framework.
- Incorporated a residual network for efficiency and a global convolutional network (GCN) with a boundary refinement (BR) module to enhance segmentation accuracy.
- Utilized a dual-path structure to capture both channel and spatial information, addressing unclear boundaries and complex backgrounds.
Main Results
- Achieved 93.89% Mean Intersection over Union (MIoU) and 97.32% Pixel Accuracy (PA) on the ORFD dataset.
- Demonstrated real-time performance with a speed of 62.49 Frames Per Second (FPS).
- Outperformed existing advanced real-time semantic segmentation models in accuracy and speed.
Conclusions
- The proposed model offers a promising solution for real-time, high-accuracy drivable area recognition essential for autonomous vehicles.
- The integration of dual attention mechanisms and lightweight design ensures efficient and accurate performance in dynamic, unstructured environments.
- This advancement contributes to the safe and efficient operation of autonomous vehicles.
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