为解决卷积神经网络（CNN）在二维路面灰度图像裂缝自动检测中存在的识别效率和精确度低的问题，首先提出了一套基于转置CNN层间特征融合的三阶段路面裂缝提取算法（该算法包括区域判定、图像分割、多层特征融合等模块）；然后构建了分类-分割网络，训练了多个融合分类网络中间层和分割网络输出层的转置卷积网络，并与CrackNet进行了运行效果的对比。结果表明：当用于区域判定的分割网络CNN-Ⅰ的召回率最小值设置为0.95时，精确度为0.497，此时的阈值为0.003152，结合用于裂缝提取的分割网络CNN-Ⅱ的训练结果得出，分类-分割网络的精确度为0.78、召回率为0.73、F-1分数为0.75、计算一张图片的时间缩短到0.79ms以内；多层特征融合方法提取裂缝信息更准确，保留了裂缝的连续性特征，实现了基于CNN的路面裂缝自动识别和提取的优化。

To solve the problem of low recognition efficiency and accuracy of Convolutional Neural Network (CNN) in automatic detection of gray image cracks in two-dimensional pavement, this paper proposed a three-stage road crack extraction algorithm based on feature fusion between layers of transposed CNN. The algorithm includes area judgment, image segmentation, multi-layer feature fusion and other modules. Then this study constructed a classification segmentation network and trained several transposed convolution networks of multi-fusion classification network intermediate layer and divided network output layer. Their operation effect was compared with that of CrackNet. The results show that when the minimum recall rate of CNN-Ⅰ is set to 0.95, the accuracy is 0.497, and the threshold value is 0.003152. According to the training results of CNN-Ⅱ, the accuracy of classification segmentation network is 0.78, recall rate is 0.73, F-1 score is 0.75, and the time for calculating a picture is shortened to less than 0.79 ms. The crack information extracted by multi-layer feature fusion method is more accurate because this method retains the continuity of the crack and realizes the optimization of automatic recognition and extraction of pavement cracks based on CNN.