Conventional algorithms are not sensitive to small objects like pavement cracks. We developed a pavement crack detection method based on a convolutional neural network (CNN) with multiple feature layers. The model extracts multi-scale features to increase the accuracy of pavement crack recognition. After hyperparameters tuning, the model accuracy reached 98.217%, and the detection rate reached 96.6 frame per second (FPS). These results showed that the model could be feasibly used for real-time crack detection. Using multiple aspect ratio anchor boxes and multi-scale feature maps, the accuracy can be improved by 1.809% and 5.016%, respectively. Compared with the traditional detection algorithm, our model was optimal in terms of F1 score and Precision-recall curve, and it was less affected by shadows and road markings and detected the crack boundaries more accurately. An on-site crack detection experiment was carried out to quantify the effectiveness of the model in crack detection.

传统算法对路面裂缝等小物体不敏感。我们开发了一种基于具有多个特征层的卷积神经网络 (CNN) 的路面裂缝检测方法。该模型提取多尺度特征以提高路面裂缝识别的准确性。经过超参数调优后，模型准确率达到 98.217%，检测率达到 96.6 帧/秒（FPS）。这些结果表明，该模型可用于实时裂纹检测。使用多纵横比锚框和多尺度特征图，准确率可以分别提高 1.809% 和 5.016%。与传统的检测算法相比，我们的模型在 F1 分数和 Precision-recall 曲线方面是最优的，并且受阴影和道路标记的影响较小，并且更准确地检测到裂缝边界。进行了现场裂纹检测实验，以量化模型在裂纹检测中的有效性。