Blind lane recognition based on context salient attention mechanism and transfer learning

Lin Li

doi:10.1117/12.3023724

14 March 2024 Blind lane recognition based on context salient attention mechanism and transfer learning

Lin Li

Proceedings Volume 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023); 1307407 (2024) https://doi.org/10.1117/12.3023724
Event: Fifth International Conference on Image, Video Processing and Artificial Intelligence, 2023, Shenzhen, China

Abstract

It’s very helpful for the person with visual impairments to assist in detecting blind lane, A novelty real-time detection for blind lane detection based on context salient attention mechanism and transfer learning is proposed. The method is based on transfer learning and feature integration with the visual attention mechanism. Firstly, the bottleneck descriptors and salient attention-based features are extracted through transformer-like feature integration, and then these features are incorporated together. Secondly, we train the new model based on a pre-trained model with careful parameter tuning. In the experiments, samples of blind lane images are collected at different areas in Chengdu for training and validation with different model cases. The experimental results show that the proposed method has advantages in classification precision by directly using Swin transformer by transfer learning with an attention mechanism. Our method is better than the original one, and the total accuracy is increased from 93.98% to 96.03% on the standard flower photos dataset. We got a total precision of 99% on our own blind road datasets.

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

Citation Download Citation

Lin Li "Blind lane recognition based on context salient attention mechanism and transfer learning", Proc. SPIE 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023), 1307407 (14 March 2024); https://doi.org/10.1117/12.3023724

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KEYWORDS

Data modeling

Machine learning

Roads

Deep learning

Education and training

Transformers

Image classification

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