# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
###### Abstract  
For the development of reinforced concrete structures and infrastructure construction, traditional rebar checking and acceptance methods have shortcomings in terms of efficiency. The use of digital image processing technology cannot easily identify a rebar configuration with complex and diverse backgrounds. To solve this problem, an inspection method combining deep learning and digital image processing techniques is proposed using an improved DeeplabV3+ model to identify reinforcing bars, with the identification results subjected to digital image processing operations to obtain the size information of the reinforcing bar. The proposed method was validated through a field test. The results of the experiment indicated that the proposed model is more accurate than other models, with a mean Intersection over Union (mIoU), precision, recall, and F1 score reaching 94.62%, 97.42%, 96.95%, and 97.18%, respectively. Moreover, the accuracy of the dimension estimations for the test reinforcements met the engineering acceptance standards. intelligent detection; rebar size measurement; DeeplabV3+ model; attention mechanism +
Footnote †: journal: Applied  
0000-0001-4000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-000000-00000-0  
Many attempts have been made to apply deep learning algorithms in the construction industry. [PERSON] et al. [18]. used an improved Faster R-CNN algorithm to extract features of helmets using VGG16 as a feature extraction network, which can detect whether helmets were worn or not, with high accuracy. [PERSON] et al. [19] used the lightweight network PP-LCNet as the backbone network of the YOLOv4 model, and used deep separable convolution to significantly improve the detection speed while ensuring the accuracy of helmet detection. [PERSON] et al. [20] proposed a dense-end face detection algorithm based on YOLOv5, which reduced repetitive gradient information by introducing cross-stage connections and incorporating an attention mechanism to improve the feature extraction capability of the network. In terms of structural health monitoring, [PERSON] et al. [21] designed a real-time crack detection system using the YOLOv3-tiny model as well as laser sensors. [PERSON] et al. [22] used the DeeplabV3+ model combined with unmanned aerial vehicle inspection to identify concrete shedding and exposed reinforcements on concrete bridge surfaces. [PERSON] et al. [23] used deep learning methods to process bridge GPR data for the rapid detection and localization of bridge deck reinforcements. Regarding rebar detection, [PERSON] et al. [24] proposed a multi-scale steel bar detection network RebarNet based on the YOLOv5 embedded attention mechanism, which can effectively reduce missed and false detections in real-time detection-based steel bar counting detection tasks. [PERSON] et al. [25] developed an automated system based on convolutional neural network (CNN) computer vision technology for estimating the size of steel bars in bundled packaging and counting the quantity. [PERSON] et al. [26] added bottom-up path and attention mechanisms to the Mask R-CNN model to obtain a more accurate rebar target detection. However, their subsequent treatment of rebars still relied on traditional image processing techniques, limiting its applicability to relatively narrow scenarios. [PERSON] et al. [27] improved the Harris corner detection algorithm and combined it with a laser rangefinder to propose an image-based intelligent ranging system for measuring steel bar spacing. However, this method requires the manual setting of relevant parameters for different scenarios. While deep learning techniques have been extensively adopted in the construction industry, there remains a research gap in applying this technology to the detection of rebar engineering dimensions. Currently, there is still a lack of a fast, convenient, highly efficient, and widely applicable intelligent method for detecting rebar dimensions.