Steel Bar Counting from Images with Machine Learning

Hernández-Ruiz, A.C.; Buldain-Pérez, J.D.; Martínez-Nieto, Javier Alejandro

doi:10.3390/electronics10040402

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…To validate the proposed counting model, a comparison of this study and similar research was conducted. Hernández-Ruiz et al [48] designed an SA-CNN-DC model, adopting binary classification and distance clustering to automatically count squared steel bars from images. Ghazali et al [49] proposed a steel detection and counting algorithm adaptable to rectangular steel bars.…”

Section: Discussionmentioning

confidence: 99%

Automated Counting of Steel Construction Materials: Model, Methodology, and Online Deployment

Chen,

Huang,

Chen

et al. 2024

Buildings

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Construction material management is crucial for promoting intelligent construction methods. At present, the manual inventory of materials is inefficient and expensive. Therefore, an intelligent counting method for steel materials was developed in this study using the object detection algorithm. First, a large-scale image dataset consisting of rebars, circular steel pipes, square steel tubes, and I-beams on construction sites was collected and constructed to promote the development of intelligent counting methods. A vision-based and accurate counting model for steel materials was subsequently established by improving the YOLOv4 detector in terms of its network structure, loss function, and training strategy. The proposed model provides a maximum average precision of 91.41% and a mean absolute error of 4.07 in counting square steel tubes. Finally, a mobile application and a WeChat mini-program were developed using the proposed model to allow users to accurately count materials in real time by taking photos and uploading them. Since being released, this application has attracted more than 28,000 registered users.

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Section: Discussionmentioning

confidence: 99%

Automated Counting of Steel Construction Materials: Model, Methodology, and Online Deployment

Chen,

Huang,

Chen

et al. 2024

Buildings

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“…They used a Receptive Field Block (RFB)-Feature Pyramid Networks (FPN) model to localise and classify the rebars, which produced a maximum F1 score of 98.17%. Hernández-Ruiz et al (2021) developed a method for counting rebars inside a warehouse. CNN was used to establish whether there were rebars within cropped areas and DC was used to estimate the possible centres of the rebars.…”

Section: Deep Learning Approachmentioning

confidence: 99%

The Development of a Rebar-Counting Model for Reinforced Concrete Columns: Using an Unmanned Aerial Vehicle and Deep-Learning Approach

Wang,

Kim,

Hae

et al. 2023

J. Constr. Eng. Manage.

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Inspecting the number of rebars in each column of a reinforced concrete (RC) structure is a significant task that has to be undertaken during the rebar inspection process. Conventionally, counting the rebars has relied on a manual inspection carried out by visiting inspectors. However, this approach is very time-consuming, labourintensive, and poses a potential safety risk. Previous studies have focused on the applications of counting the rebars for a production line and/or warehouse, using vision-based methods. Therefore, this study aims to propose an innovative approach incorporating the use of an unmanned aerial vehicle (UAV) on real construction sites to count the rebars automatically. For analysing the images, robust object detection methods based on deep learning (Faster R-CNN, R-FCN, SSD 300, SSD500, YOLOv5, and YOLOv6) were developed. A total of 384 models generated from six different methods were trained and implemented using datasets based on the original and augmented images with adjustments made for the hyperparameters. In a test, the best optimised model based on Faster R-CNN produced an accuracy of 94.61% at AP50. In addition, video testing demonstrated a coverage of up to 32 frames per second in the experimental environment, suggesting that this method has potential for real-time application. Practical applicationsDrones provide an efficient way to monitor the number of rebars in reinforced columns by capturing still images or video footage. However, manually counting the rebars from this data in the form of images is both time-consuming and laborious. This research therefore develops an AI-driven technique, based on deep learning, designed to automate the process. In the experiment, the approach that was developed achieved an accuracy rate of 94.61% under diverse conditions on real construction sites, including non-uniform illumination and complex backgrounds (e.g., scaffolding and moulding). Nevertheless, there is potential for further improvement in certain scenarios (e.g., where there are shadows in high-illumination images, or similar objects close to the rebars). In addition, video testing demonstrated that the system could process up to 32 frames per second. Despite its limitations, the method developed in this research could be put to practical use on construction sites, except in those scenarios where it showed a lower rate of accuracy. Moreover, as 30 frames per second is often regarded as equivalent to real-time, it would also be feasible to use it for video analytics' applications such as real-time monitoring and progress tracking.

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“…This study showed that it achieved a 99.26% steel rebar counting accuracy and 4.1% of the center offset for center localization on the steel rebar datasets. Similarly, Hernández-Ruiz et al [12] counted steel rebars from images using SA-CNN-DC (Scale Adaptive-Convolutional Neural Network-Distance Clustering) to improve accuracy with low-computing resources, which is frequently pointed out as one of the challenges in machine learning research. The used methods in this study would make it possible to count steel rebars regardless of size and to indicate satisfactory results with low-computing resources.…”

Section: Related Workmentioning

confidence: 99%

“…In general, steel bars are manufactured from steel mills and transported to construction sites in bale packing. According to previous studies [8][9][10][11][12], the number of steel bars should be counted in bale packing before they leave the factory and after they arrive at the construction site. However, the practice of quantifying steel rebars in South Korea is measured by weight in steel mills to enhance the speed and efficiency of shipping them.…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

Shin¹,

Heo²,

Han³

et al. 2021

Buildings

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Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920×1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars.

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Steel Bar Counting from Images with Machine Learning

Cited by 7 publications

References 21 publications

Automated Counting of Steel Construction Materials: Model, Methodology, and Online Deployment

Automated Counting of Steel Construction Materials: Model, Methodology, and Online Deployment

The Development of a Rebar-Counting Model for Reinforced Concrete Columns: Using an Unmanned Aerial Vehicle and Deep-Learning Approach

An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

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