基于增强卷积神经网络的路面裂纹检测

为了提高路面裂纹检测的效率以及精度，将增强卷积神经网络引入路面裂纹图像识别中。首先，采用线性灰度变换对原始图像进行预处理，减少噪声对识别的影响。接着经过结构设计，算法训练以及实验样本测试几个步骤后，建立了路面裂纹识别模型。最终通过在Matlab实验显示，建立的识别模型能够有效地对路面裂纹进行识别，识别率可达92.8%。实验结果表明相比于其他算法，本算法具有效率高、结果准确等优势，能够满足工程需求。

Pavement Crack Detection Based on Enhanced Convolution Neural Network

In order to improve the efficiency and accuracy of pavement crack detection, the enhanced convolution neural network is introduced into the recognition of pavement crack image. First, the original image is preprocessed by linear gray transformation to reduce the influence of noise on recognition. After several steps, such as structure design, algorithm training and experimental sample testing, the pavement crack identification model is established. Finally, through the experiments in MATLAB, the recognition model can effectively identify the pavement cracks, and the recognition rate can reach 92.8%. Experimental results show that compared with other algorithms, the algorithm proposed in this paper has the advantages of high efficiency, accurate results, and can meet the engineering needs.