Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

作     者：Hyun Kyu Shin Si Woon Lee Goo Pyo Hong Lee Sael Sang Hyo Lee Ha Young Kim 

作者机构：Architectural EngineeringHanyang UniversityERICAAnsan15588Korea Department of Artificial IntelligenceAjou UniversitySuwon16499Korea Division of Architecture and Civil EngineeringKangwon National UniversitySamcheok25913Korea Division of Smart Convergence EngineeringHanyang UniversityERICAAnsan15588Korea Graduate School of InformationYonsei UniversitySeoul03722Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第66卷第3期

页      面：2493-2507页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：a grant(19CTAP-C152020-01)from Technology Advancement Research Program(TARP)funded by the Ministry of Land Infrastructure and Transport of the Korean government 

主　　题：Faster R-CNN deep learning defect detection concrete structures 

摘      要：The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of ***,conventional manpower-based inspection methods not only incur considerable cost and time,but also cause frequent disputes regarding defects owing to poor ***,the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent,as the reduction in maintenance costs is significant from a long-term ***,this paper proposes a deep learning-based image objectidentification method to detect the defects of paint peeling,leakage peeling,and leakage traces that mostly occur in underground parking lots made of concrete *** deep learning-based object-detection method can replace conventional visual inspection methods.A faster region-based convolutional neural network(R-CNN)model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface *** defects were classified according to their type,and the learning of each exclusive model was ensured through test sets obtained from real underground parking *** a result,average precision scores of 37.76%,36.42%,and 61.29%were obtained for paint peeling,leakage peeling,and leakage trace defects,***,this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.