针对传统方法对于路面状态识别准确率低及人工提取路面图像特征过程复杂等问题，提出了一种改进ResNet的路面状态识别算法，对7种路面状态进行识别。首先，在ResNet经典模型算法上添加Dense Block密连模块，使算法提取路面状态的更多浅层边缘特征，然后在ResNet的残差模块中引入双注意力机制，使算法根据不同通道特征的重要程度，自适应提取深层关键特征；最后，添加两层全连接层并引入Dropout抑制过拟合，最终实现路面状态的识别。实验结果表明：该方法的准确率、精确率、召回率、F₁ score和特异度均达到99.0%以上，能有效提取关键的特征信息，具有较强的鲁棒性，有效地提高了路面状态的识别精度。

A modified ResNet road state recognition algorithm was proposed to address the problems of low accuracy in traditional methods for road state recognition and the complex process of manually extracting road image features, which can recognize 7 types of road states. Firstly, a Dense Block dense connection module is added to the ResNet classic model algorithm to extract more shallow edge features of the road surface state. Then, a dual attention mechanism was introduced in the ResNet residual module to adaptively extract deep key features according to the importance of different channel features. Finally, by adding two fully connected layers and introducing Dropout to suppress overfitting, the recognition of road surface states was ultimately achieved. The experimental results show that the accuracy, precision, recall, F₁ score and specificity of the proposed method all reach over 99.0%. It can effectively extract key feature information and has strong robustness, effectively improving the recognition accuracy of road surface states.