Detecting subsurface diseases on airport road surface based on an improved SSD algorithm
Mengmeng Pan, Huiguang Chen, Lipeng Yang, XianRong Jiang
TL;DR
This paper introduces an improved SSD algorithm to automatically detect subsurface road diseases at airports, improving detection accuracy and safety.
Contribution
The novel EFA-SSD algorithm enhances subsurface disease detection by integrating an attention mechanism and feature aggregation.
Findings
EFA-SSD achieves the highest mean average precision (mAP) in detecting four types of subsurface diseases.
The algorithm effectively suppresses noise and enhances feature extraction for accurate classification and localization.
The model's performance outperforms existing classical target detection algorithms in airport road subsurface disease detection.
Abstract
Due to the frequent impact of aircraft takeoff and landing and the influence of weather temperature changes, airport roads will have different types of underground diseases (DT-CRACK, DT-GAP, DT-LACUNAS and DT-SUBSIDENCE), which affect the road performance and service life, cause safety accidents, and result in a great loss of manpower and material resources. Facing the radar data of underground hidden diseases of airport roads with low recognition and high noise intensity, it is inefficient to recognize the diseases by manual identification, and it is difficult to achieve accurate differentiation and localization of the diseases by the existing detection methods. We analyze and propose an improved algorithm EFA-SSD (Enhanced Feature Aggregation SSD) for automatic detection of airport road subsurface diseases, which solves the problems of strong noise background conditions, severe…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Geophysical Methods and Applications