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ACNet: An Attention-Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous

Qiliang Zhang^1,2, Kaiwen Hua^1,2,3, Zi Zhang^1,2,3

¹School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces novel deep learning modules to improve road scene semantic segmentation for autonomous driving. The new approach enhances object boundary recognition and irregular object perception, boosting overall environmental perception accuracy.

Keywords:

attention mechanism autonomous driving convolution deep learning semantic segmentation vehicle-mounted cameras

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Driving Systems

Background:

Semantic segmentation is vital for AI in intelligent vehicular networks, enabling environmental perception and safety.
Current deep learning methods struggle with balancing global/local features (blurred boundaries) and perceiving irregular objects (information loss).

Purpose of the Study:

To enhance the accuracy and robustness of semantic segmentation in road scenes for autonomous driving applications.
To address limitations in current deep learning models regarding feature representation and irregular object recognition.

Main Methods:

Proposed a global-local collaborative attention module for enhanced feature representation via bidirectional interaction and dynamic weighting.
Introduced a spider web convolution module with asymmetric sampling and multi-directional receptive fields for improved irregular object recognition.

Main Results:

The proposed method achieved excellent performance across multiple metrics (mIoU, mRecall, mPrecision, mAccuracy) on Cityscapes, CamVid, and BDD100K datasets.
Comparative experiments confirmed the superiority of the proposed modules over classical methods and existing attention/convolution techniques.

Conclusions:

The developed approach significantly improves sensor-based semantic segmentation for autonomous driving.
The method is well-suited for environmental perception systems, enhancing vehicle safety and decision-making capabilities.