A real-time crack detection algorithm for pavement based on CNN with multiple feature layers

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.

Automated summary

A pavement crack detection method based on a convolutional neural network was proposed, which achieved high accuracy and detection rate through multi-scale feature extraction. Experimental results showed the model’s feasibility in real-time crack detection and improved accuracy using multiple aspect ratio anchor boxes and multi-scale feature maps.