Citation

Dang X, Shang X, Hao Z, Su L. Electronics (Basel) 2022; 11(20): e3304.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/electronics11203304

PMID

unavailable

Abstract

Road damage brings serious threats and inconvenience to traffic safety travel. Road damage detection and recognition can assist in eliminating the potential safety hazards in time and reduce traffic accidents. The majority of the existing road damage detection methods require significant computing resources and are difficult to deploy on resource-constrained edge devices. Therefore, the road surface data collected during the driving process of the vehicle are usually transmitted to the cloud service for analysis. However, during the driving process of the vehicle, due to problems, such as network coverage, connection, and response, it is difficult to meet the needs of real-time detection and identification of road damage. Therefore, this paper proposes a road damage classification and identification method based on edge computing. This method adds edge services. First, deep learning models are deployed on edge and cloud servers; then, a standardized entropy is set by information entropy to find the appropriate threshold as well as the best point of edge and cloud that work together to ensure high accuracy and fast response of road damage identification; finally, the cloud uses the data uploaded by the edge to assist the edge in updating the edge model. In comparison with the two cases of uploading data to the cloud server for analysis and uploading data to the edge server for analysis, the results show that the accuracy of the method is 16.21% higher than the method only executed at the edge end, and the average recognition time is 38.82% lower than the method only executed at the cloud end. While ensuring a certain accuracy, it also improves the efficiency of classification and recognition, and can meet the needs of fast and accurate road damage classification and recognition.

Language: en