Automated vision-based structural health inspection and assessment for post-construction civil infrastructure

被引：15

作者：

Agyemang, Isaac Osei ^{[1
]}

Zhang, Xiaoling ^{[1
]}

Adjei-Mensah, Isaac ^{[1
]}

Acheampong, Daniel ^{[2
]}

Fiasam, Linda Delali ^{[3
]}

Sey, Collins ^{[3
]}

Yussif, Sophyani Banaamwini ^{[4
]}

Effah, Derrick ^{[5
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 610054, Peoples R China

[2] Florida Gulf Coast Univ, Lutgert Coll, Ft Myers, FL USA

[3] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China

[4] Univ Elect Sci & Technol China, Sch Comp Sci, Chengdu 610054, Peoples R China

[5] Univ Elect Sci & Technol China, Sch Management Sci & Engn, Chengdu 610054, Peoples R China

来源：

AUTOMATION IN CONSTRUCTION | 2023年 / 156卷

基金：

中国国家自然科学基金;

关键词：

Structural health monitoring; Object detection; Drones; Deep learning; Autonomous; Reinforcement learning;

D O I：

10.1016/j.autcon.2023.105153

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

In the search to enhance the effectiveness and efficiency of structural health monitoring for preserving safety and integrity in civil infrastructure, the search for innovative technologies is of critical importance. This paper introduces ExpoDet, a comprehensive framework designed for autonomous health inspection and infrastructure assessment. ExpoDet features a multi-detection detector, autonomous navigation for micro aerial vehicles facilitated through secondary reward reinforcement learning, and a damage aggregation scheme for autonomous health assessment following detections. Moreover, it presents an attention module called EEAM+ that introduces dynamic feature orientation and significantly enhances the capabilities of ExpoDet. ExpoDet is extensively tested and evaluated in both offsite and field test experiments. Comparisons with several state-of-the-art object detectors coupled with attention modules show an average improvement of approximately 3% across various evaluation metrics.

引用

页数：18