基于改进Inception-ResNet-v2的城市交通路面状态识别算法

针对传统方法对于路面状态识别准确率低的问题,提出了一种改进Inception-ResNet-v2的路面状态识别算法,对六种城市交通路面状态进行识别。首先,在Inception-ResNet-v2算法的Inception-ResNet-C模块引入SENet注意力机制得到SE-Inception-ResNet-C模块,使算法学习到不同通道特征的重要程度；然后采用特征融合策略,将不同层级的特征信息融合,防止重要特征信息的丢失；最后采用全卷积结构,将原始算法中的全连接层换成卷积层,不仅保证了图像的空间结构,还能使网络接收任意尺度的图片。实验结果表明,该算法能提取关键的特征信息,有效提高了路面状态的识别精度。

Urban Traffic Road Surface Condition Recognition Algorithm Based on Improved Inception-ResNet-v2

Aiming at the problem that traditional methods have low accuracy in road surface condition recognition, an improved Inception-ResNet-v2 algorithm for road surface condition recognition is proposed to identify six kinds of urban traffic road surface conditions. First of all, SENet attention mechanism was introduced into the Inception-ResNet-C module of Inception-ResNet-v2 algorithm to obtain SE-Inception-ResNet-C module, in order to learn the importance of different channel features; then feature fusion strategy was used to infuse feature information of different levels to avoid losing vital feature information; at last, fully convolutional structure is used to replace fully connected layer in original algorithm with convolutional layer, thus the space structure of the image is ensured, and any dimension of images can be received by the network. The experiment result shows that key feature information can be extracted by this algorithm, and the recognition accuracy of road surface condition is effectively improved.