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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

Detecting subsurface diseases on airport road surface based on an improved SSD algorithm.

Mengmeng Pan¹, Huiguang Chen¹, Lipeng Yang²

¹Cloud Network Operating System Research and Development Center, China Telecommunications Corporation, Beijing, China.

|July 23, 2025

Summary

This summary is machine-generated.

A new Enhanced Feature Aggregation SSD (EFA-SSD) algorithm accurately detects airport road subsurface diseases from noisy radar data. This method improves recognition and localization, enhancing aviation safety.

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Area of Science:

Geotechnical Engineering
Artificial Intelligence
Radar Remote Sensing

Background:

Airport road subsurface diseases (cracks, gaps, lacunas, subsidence) degrade performance and safety due to aircraft operations and weather.
Manual identification of these diseases from radar data is inefficient and inaccurate due to high noise and feature interference.

Purpose of the Study:

To develop an improved algorithm for automatic detection, classification, and localization of airport road subsurface diseases.
To address challenges of high noise, feature interference, and low recognition rates in radar data.

Main Methods:

Proposed an Enhanced Feature Aggregation SSD (EFA-SSD) algorithm incorporating a wider receptive field RFB module to suppress noise and extract features.
Fused shallow network features to capture detailed textures of different disease types.
Integrated a spatial channel attention mechanism to enhance feature expression and model generalization.

Main Results:

The EFA-SSD algorithm demonstrated superior performance in detecting four types of subsurface diseases.
Achieved the highest mean average precision (mAP) compared to existing classical target detection algorithms.
Effectively suppressed noise interference and improved the recognition and localization of diseases.

Conclusions:

EFA-SSD offers a novel and effective approach for automatic detection of airport road subsurface diseases from radar data.
The algorithm enhances detection accuracy and contributes to improved aviation safety and infrastructure maintenance.