Research on intelligent recognition algorithm for common roadbed diseases based on radar images

Dayong Wang; Mingzhou Bai; Gang Tian; Yanli Qi; Zelin Li; Hongyu Liu

doi:10.1117/12.3009447

16 October 2023 Research on intelligent recognition algorithm for common roadbed diseases based on radar images

Dayong Wang, Mingzhou Bai, Gang Tian, Yanli Qi, Zelin Li, Hongyu Liu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030C (2023) https://doi.org/10.1117/12.3009447
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

In China, the frequency and variety of roadbed diseases have been increasing, leading to a rise in road safety accidents caused by such issues. Identifying the location, morphology, and development stage of these hidden roadbed diseases is crucial for ensuring the safe operation and maintenance of highways. Current non-destructive testing methods, like ground-penetrating radar, heavily rely on subjective data processing and interpretation. This study focuses on common roadbed diseases—roadbed looseness and voids. It establishes training and test sets using existing images in a 1:4 ratio and labels disease types in each image. The Faster R-CNN algorithm is enhanced to faster_rcnn_resnet101 and rfcn_resnet101 versions. Training both algorithms and analyzing loss, test recognition area, and accuracy reveals faster_rcnn_resnet101’s superior performance. It achieves a total loss of 0.0392, a recognition accuracy of 86.0%, compared to rfcn_resnet101’s recognition count of 324 (higher than faster_rcnn_resnet101’s 284). Considering all aspects, the faster_rcnn_resnet101 algorithm is better suited for intelligently recognizing roadbed diseases on urban roads.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dayong Wang, Mingzhou Bai, Gang Tian, Yanli Qi, Zelin Li, and Hongyu Liu "Research on intelligent recognition algorithm for common roadbed diseases based on radar images", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030C (16 October 2023); https://doi.org/10.1117/12.3009447

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KEYWORDS

Detection and tracking algorithms

Education and training

Diseases and disorders

Roads

Object detection

Radar

Neurological disorders

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