Research On Detection And Identification Method Of Concrete Pavement Cracks Based On Deep Learning

Concrete is an important material for highway construction.Due to the long-term erosion of the external environment,road cracks and other diseases are prone to occur,resulting in serious traffic accidents and great potential safety hazards.Therefore,the detection and quantitative evaluation of concrete pavement cracks is of great significance for road repair.important practical significance.At present,the deep learning method has become the main way to complete the tasks related to concrete pavement cracks.By fully learning the characteristics of pavement cracks,it can better adapt to the complex background in the concrete pavement image,and the concrete pavement crack detection algorithm based on deep learning has high’s accuracy.By analyzing the feature differences between road background and cracks in concrete pavement crack images,this paper proposes a pixel-level semantic segmentation algorithm for pavement cracks based on DeepLabV3Plus.In order to improve the extraction speed and segmentation effect of pavement cracks,the backbone network Modified Aligned Xception is replaced with a lightweight network MobileNetV2 to accelerate the extraction of crack image features;At the same time,the ASPP module is adjusted from parallel connection to cascade splicing,which expands the receptive field of the model and enhances the correlation of each branch;Furthermore,two shallow features are fused in the decoder structure,which provides more detailed information for the segmentation results.Aiming at the problem of class imbalance between the road background and cracks in the image,the algorithm uses the corresponding class weighting method to correct the loss function.The improved model achieves MIoU and MAP of 75.18%and 83.64%respectively on the pavement crack dataset constructed in this paper,and the segmentation time of a single image is shortened to 58.2ms.Compared with the original segmentation model,the segmentation speed is greatly improved,indicating that the model It can quickly and effectively segment and extract pavement cracks.In order to classify and quantitatively evaluate pavement cracks based on the extraction results,this paper proposes a pavement crack classification model based on MobileNetV2,and establishes a pavement crack evaluation system.The classification model integrates multi-scale features and CBAM attention mechanism,strengthens the attention to the effective information of crack images,and improves the accuracy of crack image classification.In the pavement evaluation system,the crack length,average width,area and damage ratio are calculated respectively to quantify the crack parameters,and the pavement condition is evaluated according to the pavement grade classification standard.The experimental results show that the accuracy of the classification model in this paper in the crack binary image classification dataset reaches 90.81%,which is 2.79%higher than that of MobileNetV2.The model can quickly classify crack binary images.In addition,according to the quantitative results of pavement cracks and the relative error rate,it shows that the extraction of cracks in this paper is more accurate,which is in line with the actual situation of concrete pavement cracks.