国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Aiming at the insufficient ability of the traditional convolutional neural network model to identify long-distance crack structure and the accuracy limitation in the detection of asphalt pavement disease, the Swin Transformer model was introduced to study the classification of asphalt pavement disease. First of all, for the problem of low contrast of the asphalt pavement scanning image collected by the road inspection vehicle, the histogram equalization technology is used to process the image to increase the image visualization effect. Secondly, three classic convolutional neural network models are selected as comparison models, and methods such as replacing the loss function and adjusting the pre-training model are used to solve the over-fitting problem during the training process. And select accuracy rate, recall rate, F1-score as the evaluation index. In the final experimental results, the recognition accuracy of Swin Transformer reached 80.6%, and the F1-score reached 0.776, which not only surpassed the traditional CNN model in overall classification accuracy, but also had a higher recognition of diseases with long-distance characteristic structures accuracy and good reliability.