Highway Pavement Safety Detection System Based on Deep Learning Algorithm

The safety inspection of highway pavement is facing problems such as low efficiency, insufficient accuracy, and high cost of manual inspection, and urgently needs an efficient and intelligent inspection system. This article aims to study a highway pavement safety detection system based on deep learning algorithms, in order to ensure driving safety by improving the automation and accuracy of detection. The article collects a large amount of road surface image data, annotates it, and constructs a dataset containing multiple types of road surface damage; subsequently, the article traines a Convolutional Neural Network (CNN) model and optimizes its performance using transfer learning techniques. During the experiment, the dataset was divided into a training set and a testing set for model training and evaluation. The results showed that the accuracy of the trained model on the test set reached 96.1%, and the detection speed remained above 15 frames per second at multiple time points, significantly improving detection efficiency and accuracy. The deep learning based highway pavement safety detection system effectively solves the shortcomings of traditional detection methods, provides reliable technical support for highway maintenance, and has broad application prospects.