Research on vision-based multi-task environment perception algorithm for intelligent driving

Bo Wang; Tianrui Hu

doi:10.1117/12.3029140

6 May 2024 Research on vision-based multi-task environment perception algorithm for intelligent driving

Bo Wang, Tianrui Hu

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131073I (2024) https://doi.org/10.1117/12.3029140
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

Existing deep learning algorithms usually process only a single detection task in environment perception, which cannot meet the driving needs of driverless vehicles. To this end, a new multi-task environment perception network is designed, which can simultaneously complete vehicle detection and lane line detection; Taking YOLOv8 as the backbone network and integrating the latest C2f module, the efficient extraction of image features is realized; the neck section adopts BiFPN (Bidirectional Feature Pyramid) structure for better feature fusion and semantic preservation; in loss calculation, αIoU is fused to improve the detection accuracy; based on semantic segmentation, the UCC module is utilized to design an efficient lane line detection branch. Comparison experiments show that the average vehicle detection accuracy reaches 80.0%, and the IoU of lane line detection is 27.10%, which is better than other multi-task perception algorithms in terms of comprehensive performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Wang and Tianrui Hu "Research on vision-based multi-task environment perception algorithm for intelligent driving", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131073I (6 May 2024); https://doi.org/10.1117/12.3029140

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KEYWORDS

Detection and tracking algorithms

Autonomous driving

Feature fusion

Education and training

Autonomous vehicles

Network architectures

Semantics

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