Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization

Abstract

Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation of infrastructures and has become a research hot spot. However, most existing vision‐based inspection methods mainly focus on detecting one kind of defect in nearly uniform testing background where defects are relatively large and easily recognizable. But in the real‐world scenarios, multiple types of defects often occur simultaneously. And most of them occupy only small fractions of inspection images and are swamped in cluttered background, which easily leads to missed and false detections. In addition, the majority of the previous researches only focus on detecting defects but few of them pay attention to the geolocalization problem, which is indispensable for timely performing repair, protection, or reinforcement works. And most of them rely heavily on GPS for tracking the locations of the defects. However, this method is sometimes unreliable within infrastructures where the GPS signals are easily blocked, which causes a dramatic increase in searching costs. To address these limitations, we present a unified and purely vision‐based method denoted as defects detection and localization network, which can detect and classify various typical types of defects under challenging conditions while simultaneously geolocating the defects without requiring external localization sensors. We design a supervised deep convolutional neural network and propose novel training methods to optimize its performance on specific tasks. Extensive experiments show that the proposed method is effective with a detection accuracy of 80.7% and a localization accuracy of 86% at 0.41 s per image (at a scale of 1,200 pixels in the field test experiment), which is ideal for integration within intelligent autonomous inspection systems to provide support for practical applications.

Concrete bridge surface damage detection using a single‐stage detector

“…They demonstrated that the SSD could obtain relatively high accuracy for some classes of defect. However, some researchers found that directly locating objects using such single‐stage detectors could compromise the accuracy of the detection (Li et al., ; L. Wang et al., ). In particular, the earliest version of YOLO could hardly detect objects with unusual aspect ratios or configurations (Redmon et al., ).…”

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

“…Therefore, a more carefully configured deep neural network (DNN) is essential for solving the true variety of real‐world problems. For example, different DNNs have been proposed to identify cracks in images with disturbing background interference (F. C. Chen & Jahanshahi, ; Kang & Cha, ; R. Li, Yuan, Zhang, & Yuan, ; Liang, ; Y. Xu, Bao et al., ). In addition, the noising issues and the layer multiplexing scheme in DNNs have been discussed (Koziarski & Cyganek, ; Ortega‐Zamorano, Jerez, Gómez, & Franco, ).…”

Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

“…Recently, computer vision-based approaches have been studied (Koch, Georgieva, Kasireddy, Akinci, & Fieguth, 2015; © 2019 Computer-Aided Civil and Infrastructure Engineering Tizani & Mawdesley, 2011), and it has been shown that there is a need for automatic detection and techniques for classification of distresses that have occurred in road structures (O'Byrne, Schoefs, Ghosh, & Pakrashi, 2013;Zalama, Gómez, Medina, & Llamas, 2014). Because various kinds of distress occur in road structures (Li, Yuan, Zhang, & Yuan, 2018;H. Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018), the goal of our work is the realization of an accurate distress classification method based on machine learning techniques using images taken of distress parts of road structures.…”