Abstract
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.
Highlights
As car ownership has risen continuously, traffic jams, delays, and accidents spiraled upward
In the 1960s, Wiesel and Hubel [3] found that their unique network structure could effectively reduce the complexity of the feedback neural network when they studied the neurons used for local sensitivity and direction selection in the cortex of cats and proposed a convolutional neural network (CNN)
This paper proposes a road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM)
Summary
As car ownership has risen continuously, traffic jams, delays, and accidents spiraled upward. Based on the SVM, Zhao et al [19] obtained the best classification model by PSO parameter optimization, classified the road types, and improved the recognition accuracy of the test image, achieving an accuracy rate of over 90% for the five basic road types. It was found that the process had a certain randomness, and the whole process including the classification algorithm was complex To solve these problems, this paper proposes a road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM). 2. Research Method for Identifying Road Surface Conditions Based on Improved ALexNet Model (RSRM). By analyzing the characteristics of actual pavement images, nine typical pavement types are selected, as shown, focusing on nine typical road surface types; 9-label SoftMax is used to replace the original classifier in the ALexNet network. Rough the above steps, the problem of road surface image classification and recognition is solved
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