Multi-task traffic scene detection algorithm based on large kernel attention

Jianchuang Qu; Kaige Wang; Zihan Shen; Can Wu; Qing Li

doi:10.1117/12.3024670

3 April 2024 Multi-task traffic scene detection algorithm based on large kernel attention

Jianchuang Qu, Kaige Wang, Zihan Shen, Can Wu, Qing Li

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780Q (2024) https://doi.org/10.1117/12.3024670
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

A network structure based on a combination of attention mechanism and large convolution kernel is proposed to address the problem of low detection accuracy of traditional multi-task traffic scene detection algorithms, which is used to perform vehicle object detection, drivable area detection and lane line detection tasks. Firstly, this paper proposes a backbone network that fuses large kernel attention mechanism and ELAN structure to improve the overall recognition accuracy of the model. Secondly, by fusing large convolution kernel attention mechanism and multi-scale information interaction mechanism, this paper designs a segmentation enhancement module to improve the model perform lane line segmentation task. Finally, by combining the characteristics and advantages of Focal loss and Tversky loss, this paper combines them as the loss function of segmentation tasks to solve the problem of sample imbalance in segmentation. The experimental results show that on BDD100K dataset, compared with traditional convolutional neural network algorithms, the recall rate of object detection of the proposed network reaches 91.1%, the accuracy of lane line detection reaches 84.0%, and the mean intersection over union of drivable area detection reaches 92.7%, which are significantly improved. Meanwhile, the proposed algorithm achieves a detection speed of 154fps on RTX3090, meeting the real-time requirements.

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

Citation Download Citation

Jianchuang Qu, Kaige Wang, Zihan Shen, Can Wu, and Qing Li "Multi-task traffic scene detection algorithm based on large kernel attention", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780Q (3 April 2024); https://doi.org/10.1117/12.3024670

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KEYWORDS

Object detection

Detection and tracking algorithms

Image segmentation

Convolution

Feature extraction

Target detection

Image processing

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