Cascaded residual attention mechanism for semantic fusion in vehicle driving

Xiaohang Li; Jianjiang Zhou

doi:10.1117/12.2674275

28 April 2023 Cascaded residual attention mechanism for semantic fusion in vehicle driving

Xiaohang Li, Jianjiang Zhou

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 1262613 (2023) https://doi.org/10.1117/12.2674275
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Multiple sensors are often used to work together in vehicle driving, but how to effectively fuse the data of each sensor is a difficult research point. The attention mechanism can assign different weights to each target, allowing more computing resources to focus on key targets, greatly improving computational efficiency and accuracy. In this paper, ResNet34 and SalsaNext are used as encoders of dual-stream networks to extract general features of images and point clouds respectively, and a cascade residual attention fusion strategy is proposed, which is used between two-stream networks to fuse features from two modalities at different encoding stages. Experiments on the SemanticKitti dataset show that this fusion strategy has better performance than single-stage fusion and PMF fusion structures.

Citation Download Citation

Xiaohang Li and Jianjiang Zhou "Cascaded residual attention mechanism for semantic fusion in vehicle driving", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 1262613 (28 April 2023); https://doi.org/10.1117/12.2674275

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KEYWORDS

Data fusion

Point clouds

Semantics

Feature fusion

Convolution

Roads

Education and training

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