Methodology for Selecting Neural Network Architecture for Recognizing Pavement Defects

A methodology for selecting a neural network architecture for recognizing pavement defects is proposed. The defect list detected by the computer video scanning system of the mobile complex for the multi-criteria assessment of the operational condition of street and road network objects “ADS-MADI” is specified. The analysis results of several primary neural network architectures that satisfy the requirements for the highest possible accuracy with sufficient cost-effectiveness of their use are presented. The topology of the DeepLabV3 neural network is presented for the input images that were collected during the monitoring of the Moscow street and road network by a hardware and software complex consisting of linear cameras, making it possible to obtain homogeneous images of the roadbed without splices and the use of specialized software. The training results of the neural network for 25 epochs using the weights pre-trained on the Pascal VOC 2007 dataset are presented, along with plots of the error and change functions of F1 and IoU during training. The results of hyperparameter optimization using the Bayesian optimization-based HyperBand algorithm are contained, significantly reducing errors and improving the accuracy of the neural network. Results comparing the performance of the three best-fit architectures are presented.