| Citation: | NIU G C，WANG X N. A multi-task traffic scene detection model based on cross-attention[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（5）：1491-1499 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0610
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Perception is the foundation and key issue to autonomous driving, but most single models can’t simultaneously complete multiple detection tasks such as traffic objects, drivable areas, and lane lines. This paper proposes a multi-task traffic scene detection model based on cross-attention, which can detect traffic objects, drivable areas and lane lines simultaneously. Firstly, the encoder-decoder network is used to extract the initial feature maps. Subsequently, the cross-attention module obtains the segmentation and detection feature maps, and hybrid dilated convolution improves the original feature maps. Finally, the semantic segmentation is performed on the segmentation feature maps and object detection is performed on the detection feature maps. The experimental results demonstrate that, on the difficult BDD100K dataset, our model performs better than existing multi-task models in terms of task-wise accuracy and total computational efficiency.
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