An Intelligent Integrated Detection Method for Multiple Types of Pavement Distress
In order to improve the pavement distress detection efficiency and make up for the ineffi-cient and time-consuming limitations of current distress detection methods,an integrated intelligent detection method for multi-type pavement distress was proposed based on deep learning technology and combined image classification and target detection technology.The method effectively integrates three functional modules of pavement classification,pavement distress identification and pavement distress detection.Firstly,VGG-16 algorithm is adjusted and SE attention mechanism is added.Secondly,the YOLOv7 detection network is optimized,and the CBAM feedforward convolutional attention module is integrated while the small target detection layer is added.The results show that the adjusted VGG-16 network is more than 98％accurate in the task of pavement classification and pavement distress identi-fication,and the optimized YOLOv7 can improve the average accuracy of asphalt,concrete and block pavement detection by 3.00％,1.80％and 3.90％,respectively.Through field tests,the average ac-curacy of the three modules is 99.72％,98.28％and 91.52％respectively,and the comprehensive ac-curacy of the whole method is 89.69％.The results of this study provide an effective reference for the rapid screening and overall evaluation of pavement distress.
万方数据
维普
