Rebar Detection Research Articles

End Face Detection of Rebar Based on Improved PP YOLO

This paper introduces an improved PP-YOLO network method to enhance the accuracy of rebar end identification and counting in construction engineering. The network is optimized for the specific characteristics of rebar images, enhancing its recognition capabilities for rebars of various sizes and shapes. Notably, the network structure introduces new pathways in the 4th and 5th layers, enabling more effective learning and identification of rebar features from low-level characteristics, thus improving overall recognition and counting accuracy. Additionally, an optimized data augmentation strategy, tailored to the unique features of rebar images, replaces the traditional Mixup method. Specific algorithms are introduced to enhance the network's efficiency in learning rebar image characteristics. These improvements led to excellent performance in rebar end face recognition tests, achieving an average precision (AP) of 96.23%, a 1.07% increase compared to the original model. This significant performance improvement confirms the effectiveness of our proposed improvements in practical applications, offering new perspectives for the development of rebar detection technology in the construction industry.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Rebar detection: Comparison of stepped frequency continuous wave and pulsed GPR

Reinforced concrete structures are widely used in civil engineering owing to their versatility, strength, and durability. One of the most recommended methods for rebar mapping is Ground-Penetrating Radar (GPR) because of its non-destructive and non-invasive character. In concrete mapping, GPR is mainly used for locating subsurface objects (rebars, tendons, ducts), measuring concrete cover, mapping mesh configuration (spacing), and detecting bottom of slab. To achieve these, concrete scanning GPR practitioners often need very high-resolution images. This work presents a comparison in rebar mapping between two GPR systems with different modulation techniques: Stepped frequency continuous wave (SFCW) and pulsed radar. The SFCW system used has a frequency range of 400-6000 MHz, while the pulsed system used a ground-coupled central frequency antenna of 2.3 GHz. Measurements were conducted on laboratory specimens, with rebar diameters ranging from 8 to 32 mm. Three different specimens were used, with one for calibration and other two to analyze both the horizontal and vertical resolutions of the frequency antennas.

A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

Fully decouple convolutional network for damage detection of rebars in RC beams

This paper proposes a fully decouple convolutional neural network equipped with an anchor-free mechanism for the non-destructive identification of structures with internal damages. Two 3D cylinder IoU optimization and evaluation algorithms are designed to improve model training effect and to estimate model performance, respectively. The generation and preparation of the datasets is exceedingly time consuming and challenging, to overcome these difficulties, datasets of random damages of rebars in RC beams are established based on the secondary development of ABAQUS. The mapping relationship of the model takes the displacement contour image of damaged structures as the input and damage parameters of rebars as the output. The image feature extraction is performed to capture damage features in surface displacement information of the damaged structures based on proposed model, then, the structure internal damage identification is realized through multi-regression on damage parameters of rebars. To verify the efficiency of the proposed method, model comparison and numerical verification is utilized to evaluate the proposed model. The results show that the presented model has high accuracy and is superior to traditional coupled networks. The method can also be extended to the internal damage detection of other components, providing a new idea for the internal non-destructive health monitoring of prefabricated structures.

Pixel-level classification for bridge deck rebar detection and localization using multi-stage deep encoder-decoder network

Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure have been active areas of research for the past few decades. The traditional inspection methods for civil infrastructure, mostly relying on the visual inspection are time-consuming, labor-intensive, error-prone, and often provide subjective results. In the wake of rising costs for infrastructural maintenance, the time factor, safety issues, and the error-prone nature of human inspection methods, there is an increased need for the development of automated methods for bridge inspection and maintenance. The purpose of this research is to provide a novel Deep Learning-based approach for rebar detection and localization within bridge decks. The proposed system is trained using Ground Penetrating Radar (GPR) data from 8 real bridges in the United States. The results have been discussed in terms of qualitative and quantitative aspects with considerable potential and various issues that need to be explored in future works. Due to the similarity in the type of parabolic signatures present in other GPR-related applications, this technique can be generalizable to other applications. The proposed approach for rebar detection and localization has considerable implications for the civil experts in general and GPR community in particular.

Improved Lightweight Rebar Detection Network Based on YOLOv8s Algorithm

Improved Lightweight Rebar Detection Network Based on YOLOv8s Algorithm

Evaluation of Photogrammetry Tools following Progress Detection of Rebar towards Sustainable Construction Processes

In the attainment of digitization and sustainable solutions under Industry 4.0, effective and economical technology like photogrammetry is gaining popularity in every field among professionals and researchers alike. In the market, various photogrammetry tools are available. These tools employ different techniques and it is hard to identify the best among them. This study is an attempt to develop a methodology for the assessment of photogrammetry tools. Overall, 37 photogrammetry tools were found via literature review and open sources, out of which 12 tools were shortlisted. The evaluation process consisted of three steps, i.e., metadata and visual inspection, comparison with the ground truth model, and comparison with the averaged-merged point cloud model. In addition, a validation test was also performed on the final sorted photogrammetry tools. This study followed a sustainable construction progress monitoring theme for rebar and covered the maximum number of photogrammetry tools for comparison by considering the most authentic evaluation and validation techniques, which make it exclusive.

A new method and device for detecting rebars in concrete based on capacitance

This study aims at developing a convenient and efficient rebar detection method based on capacitance that can be used to precisely locate the state (i.e., diameter and position) of the rebars in concrete. Finite element analysis was used to optimize the electrode of the capacitive sensor and verify that using the developed capacitive sensor to detect rebar in concrete is feasible and reliable. Based on the detected capacitance, the position of the rebar in concrete can be located. The relationships among capacitance, rebar diameter, and cover thickness are explored. Both the peak value and the peak area of capacitance evolve linearly with rebar diameter and exponentially with cover thickness. The 3D diagrams among the capacitance peak value, peak area, and rebar diameter or cover thickness are established in this research. The experimental result agrees well with the numerical results, which verifies the reliability of the capacitive sensor and detection methods established.

Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

Depth-camera-based rebar detection and digital reconstruction for robotic concrete spraying

In this paper, we tackle the challenge of detection and accurate digital reconstruction of steel rebar meshes using a set of industrial depth cameras. A construction example under investigation in this paper is robotic concrete spraying, where material is sprayed onto double-curved single layered rebar meshes. Before the spraying process can start, the location and geometry of the rebar mesh needs to be accurately know. We present an automatic image-based processing approach of depth images for grid point extraction at an accuracy of a few mm. Furthermore, we propose a sequence of execution steps in a robotic setup, including the hand–eye calibration, which enables the direct georeferencing of multiple data sets acquired from various poses into a common coordinate system. With the proposed approach we are able to digitally reconstruct a mesh of an unknown geometry in under 10 min with an accuracy better than 5 mm. The digitally reconstructed mesh allows for computation of material needed for its construction, enabling sustainable use of concrete in digital fabrication. The accurately reconstructed digital mesh, generated based on the proposed approach in this paper, is the input for the following spraying step, allowing for generation of accurate spray trajectories.

Numerical simulation of a magnetic corrosion detector for corrosion detection of steel rebar in concrete

Numerical simulation of a magnetic corrosion detector for corrosion detection of steel rebar in concrete

Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.

Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which must be produced manually by skilled personnel. To reduce the need for training data, this study evaluates weakly and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully, weakly and semi-supervised methods for the detection of rebar covers, which are useful for quality control. In the experiments, recent models, i.e., IRNet, DeepLabv3+ and the cross-consistency training model are compared for their ability to segment rebar covers from construction site imagery with minimal manual input. The results show that weakly and semi-supervised models can indeed rival with the performance of fully supervised models with the majority of the target objects being properly found. This study provides construction site stakeholders with detailed information on how to leverage deep learning for efficient construction site monitoring and weigh preprocessing, training, and testing efforts against each other in order to decide between fully, weakly and semi-supervised training.

Reverse-Time Migration Imaging of Ground-Penetrating Radar in NDT of Reinforced Concrete Structures

The evaluation and inspection of steel bars in reinforced concrete structures are critical for prolonging the service life of buildings. In this regard, ground-penetrating radar (GPR) has been a crucial alternative due to its non-invasiveness and convenience. This paper reports the experimental activities on a test-site area inside a camp in Shanghai, China. To assess the concrete structures of the building, GPR was employed for the detection and localization of rebars in columns, beams, and floors. From the GPR B-scan profiles acquired using a high-frequency antenna, the exact quantity of reinforcements was identified according to the hyperbola responses. Considering the difficulty of inferring the exact position and the scale of the rebars, we applied reverse time migration (RTM) to collapse the hyperbolic response and retrieve the target in a migrated image. To verify the effectiveness of the RTM algorithm, we carried out an experiment on a concrete model with three reinforced bars. We also utilized the RTM algorithm to process the B-scan profiles collected in a column that was later excavated. The imaging results validated the capacity of RTM in localizing and shaping rebars. Then, we employed the RTM algorithm for the GPR B-scan data collected from the other column. Based on the imaging profile, the quantity and positions of the rebars were correctly determined. Moreover, the thickness of the protective layer was evaluated according to the migrated result. These results demonstrate that GPR combined with RTM could provide useful foundation data for structural evaluation.

Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.

A deep learning approach for real-time rebar counting on the construction site based on YOLOv3 detector

Counting steel bars is a routine daily task for steel manufacturers and most building construction sites. Currently, counting is usually performed manually, which is laborious, time-consuming, and error-prone. This study proposes a deep learning approach based on YOLOv3 detector for automatic steel bars detection and counting through images. Three new measures, including an additional feature pyramid, complete intersection over union (IoU) loss and focal loss, and bag of freebies, were introduced to improve rebar detection and counting performance. A dataset containing 74,824 rebar sections from real construction sites was constructed and utilized to evaluate the proposed approach. The application results demonstrate that the suggested measures can significantly improve the performance of the YOLOv3 detector to high average precision of 99.7% at IoU of 0.5. Comparisons with other detectors show that the proposed approach is fast, accurate, and robust at rebar counting under different construction site conditions. It can be facilitated as the basis of a real-time, cost-effective rebar counting scheme.

Application of GPR in Rebar Detection of Building Structures

The detection of rebar in pillars, beams and floor slabs of building structures is a critical issue in urbanized areas. However, due to some natural and artificial objective factors, the quantity of various rebars is not always consistent with the design data, which directly affects the quality and safety of the building. To ensure structural integrity, quality inspectors must make full use of GPR testing technology. In this paper, we discuss, compare and evaluate the geophysical detection methods and effects of GPR technology regarding the possible hidden safety hazards of reinforced concrete structures. Real testing data shows that GPR detection can provide the precise quantity and positioning of rebars within buildings.

Rebar detection and localization for bridge deck inspection and evaluation using deep residual networks

Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Due to rising costs, safety issues and error of human inspection methods, automated methods for bridge inspection and maintenance are being proposed. The purpose of this research is to develop an automated rebar detection and localization system utilizing supervised (Deep Residual Networks) and unsupervised (K- means clustering) techniques. Data has been collected from nine bridges using Ground Penetrating Radar (GPR) sensors. The performance of the proposed rebar detection and localization system has been evaluated on a wide-range of performance metrics, which emphasize the superior performance of the proposed technique over existing methods. The results reveal positive correlation between number of layers of networks, training time and other performance metrics. The overall performance of the proposed system is also dataset-dependent with factors such as noise artefacts, reflections and visual quality of rebar profiles.

Novel Method for Measurement of Rebar State of Cement Tower

Reinforced concrete (RC) structures are applied widely in low-voltage transmission towers. However, the structural damage caused by the corrosion or fracture of rebar in cement towers seriously affects the safety of transmission lines. Thus, the accurate detection of rebar can effectively maintain cement towers. There are many methods for detecting rebar corrosion, but most of them cannot accurately locate the corrosion, and they do not consider the factors such as the depth of the rebar and the different types of rebar. Therefore, this article proposes a rebar corrosion detection method based on the change of peak slope of magnetic flux density. First, this article analyzes the common pitting corrosion in engineering, then theoretically analyzes the principle of magnetic induction detection of rebar. Then the magnetic flux density peak and the measured position on the 2-D coordinate are plotted, and the rebar corrosion is detected according to the slope. The peak function of flux density without corrosion is fit by the least squares method and the corrosion criterion is defined. Finally, the proposed method is verified through simulation and experiment.

Detection and localization of rebar in concrete by deep learning using ground penetrating radar

Ground penetrating radar (GPR) has been widely used for detection and localization of reinforced steel bar (rebar) in concrete in a non-destructive way. However, manual interpretation of a large number of GPR images is time-consuming, and the results highly depend on practitioner experience and the available priori information. This paper proposes an automatic detection and localization method using deep learning and migration. Firstly, a Single Shot Multibox Detector (SSD) model is established to identify regions of interest containing hyperbolas in a GPR image. This deep learning model is trained using a real GPR dataset, which contains 13,026 rebar targets in 3992 images, collected on residential buildings under construction. Secondly, each target region is migrated and transformed into a binary image to locate the rebar. After the binarization, the apex of the focused cluster is obtained and used to estimate both the horizontal position and the depth of the rebar. The testing results show that the detection accuracy of the proposed artificial intelligence method is 90.9%. The computation time needed for processing a GPR image with a size of 300 × 300 pixels is only 0.47 s. The depth estimation error in a laboratory experiment is <1.5 mm (5%), and the lateral position error is <0.7 cm. Therefore, it is concluded that the proposed method can automatically detect the rebar from GPR images in real time when a handheld GPR system is operated at a walking speed and the depth estimation accuracy is acceptable in practice.