用于阴影干扰下路面分类的轻量级卷积神经网络

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ruichi Mao, Guangqiang Wu, Jian Wu, Xingyu Wang
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引用次数: 0

摘要

智能驾驶的发展,尤其是主动悬架的智能控制,在很大程度上依赖于对未来路况的预测感知。为了实现准确的实时路面分类并克服阴影干扰,提出了一种基于新型数据增强方法的轻量级卷积神经网络(CNN),并开发了一种改进的循环一致性对抗网络(CycleGAN)来生成阴影路面数据。利用纹理自监督(TSS)机制和学习感知图像补丁相似性(LPIPS)函数对 CycleGAN 网络结构进行了优化,并在训练过程中应用了标签平滑。这种数据增强方法生成的图像与真实世界的图像非常相似。此外,还提出了 Efficient-MBConv 方法,它具有参数少、精度高的优点。最后,基于 Efficient-MBConv 开发了 Light-EfficientNet 架构,并在增强数据集上进行了训练。与 EfficientNet-B0 相比,Light-EfficientNet 的参数数量减少了 61.94%。与未进行数据增强的模型相比,经过数据增强训练的 Light-EfficientNet 模型在有阴影的测试集上的平均分类准确率提高了 5.76%。这种方法以较低的成本有效地减少了阴影对道路分类的影响,同时还大大减少了 CNN 所需的计算资源,为控制主动悬架高度和阻尼提供了实时、准确的路面信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A lightweight convolutional neural network for road surface classification under shadow interference
The development of intelligent driving, especially in the intelligent control of active suspension, heavily relies on the predictive perception of upcoming road conditions. To achieve accurate real-time road surface classification and overcome shadow interference, a lightweight convolutional neural network (CNN) based on a novel data augmentation method is proposed and an improved cycle-consistent adversarial network (CycleGAN) is developed to generate shadowed pavement data. The CycleGAN network structure is optimized using the texture self-supervised (TSS) mechanism and the learned perceptual image patch similarity (LPIPS) function, with label smoothing applied during training. The images produced by this data augmentation method closely resemble real-world images. Furthermore, Efficient-MBConv, which offers the advantages of fewer parameters and higher precision, is proposed. Finally, the Light-EfficientNet architecture, based on Efficient-MBConv, is developed and trained on the augmented dataset. Compared with EfficientNet-B0, the number of parameters in Light-EfficientNet is reduced by 61.94 %. The Light-EfficientNet model trained with data augmentation demonstrates an average classification accuracy improvement of 5.76 % on the test set with shadows, compared with the model trained without data augmentation. This approach effectively reduces the impact of shadows on road classification at a lower cost, while also significantly reducing the computational resources required by the CNN, providing real-time and accurate road surface information for the control of active suspension height and damping.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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