ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving
In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges...
Saved in:
| Main Authors: | Qiliang Zhang, Kaiwen Hua, Zi Zhang, Yiwei Zhao, Pengpeng Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4776 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A dual attention mechanism semantic segmentation method for autonomous driving
by: WANG Yannian, et al.
Published: (2023-12-01) -
CMHT autonomous dataset: A multi-sensor dataset including radar and IR for autonomous drivingMacDrive by McMaster UniversityFederated Research Data Repository
by: Howard Zhang, et al.
Published: (2025-06-01) -
Insights of semantic segmentation using the DeepLab architecture for autonomous driving
by: Javed Subhedar, et al.
Published: (2025-06-01) -
Discriminative Cross-Modal Attention Approach for RGB-D Semantic Segmentation
by: emad mousavian, et al.
Published: (2025-04-01) -
An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving
by: Jia Tian, et al.
Published: (2025-07-01)