Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.

中文翻译：

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基于改进ALexNet模型的道路粘连状况识别研究

汽车智能化已成为汽车技术的革命性趋势。复杂的道路驾驶条件直接影响驾驶的安全性和舒适性。因此，通过提高道路类型或道路附着系数的识别精度，车辆对周围环境的感知能力将得到增强。这将进一步促进车辆智能化。本文考虑到人工提取图像特征的过程复杂且提取方法对每个人都是随机的，提出了一种基于改进的ALexNet模型的路面状况识别方法，即路面识别模型（RSRM）。 . 首先，ALexNet 网络模型在 ImageNet 数据集上离线预训练。二、训练后的浅层网络结构的权重，包括卷积层，被保存并迁移到所提出的模型。另外，将固定在浅层网络上的全连接层替换为2~3层，提高了训练精度，缩短了训练时间。最后，比较了传统机器学习和改进的 ALexNet 模型，重点关注适应性、预测输出和错误性能等。结果表明，所提模型的准确率优于传统机器学习方法 10% 和 ALexNet 模型 3%，训练速度比 ALexNet 快 0.3 h。经验证，RSRM有效提高了道路图像识别的网络训练速度和准确率。将固定在浅层网络上的全连接层换成2~3层，提高了训练精度，缩短了训练时间。最后，比较了传统机器学习和改进的 ALexNet 模型，重点关注适应性、预测输出和错误性能等。结果表明，所提模型的准确率优于传统机器学习方法 10% 和 ALexNet 模型 3%，训练速度比 ALexNet 快 0.3 h。经验证，RSRM有效提高了道路图像识别的网络训练速度和准确率。将固定在浅层网络上的全连接层换成2~3层，提高了训练精度，缩短了训练时间。最后，比较了传统机器学习和改进的 ALexNet 模型，重点关注适应性、预测输出和错误性能等。结果表明，所提模型的准确率优于传统机器学习方法 10% 和 ALexNet 模型 3%，训练速度比 ALexNet 快 0.3 h。经验证，RSRM有效提高了道路图像识别的网络训练速度和准确率。和错误性能等。结果表明，所提模型的准确率比传统机器学习方法提高10%，ALexNet模型提高3%，在训练速度上比ALexNet快0.3 h。经验证，RSRM有效提高了道路图像识别的网络训练速度和准确率。和错误性能等。结果表明，所提模型的准确率优于传统机器学习方法 10% 和 ALexNet 模型 3%，训练速度比 ALexNet 快 0.3 h。经验证，RSRM有效提高了道路图像识别的网络训练速度和准确率。