# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
###### Abstract
To solve the current application needs of intelligent inspection and to achieve the detection of a concealed reinforcement's configuration quickly and conveniently, this study innovatively combines smartphones with advanced deep learning technology to propose a new non-contact method for detecting the dimensions of rebars, without the need to set specific feature extractors based on specific scenes, making it more widely applicable, and there is very limited existing research in this area. It utilizes smartphones as capture devices to capture photos of rebars and then employs the DeeplabV3+ [28] model for automatic rebar configuration recognition. Compared to traditional manual measurement methods, the approach in this paper is more efficient, convenient, and rapid. Furthermore, this study applies advanced deep learning technology to the engineering domain, which is conducive to advancing the intelligence in the field of engineering. However, due to the original design intent of the DeeplabV3+ model not being focused on segmenting rebar objects, it faces the following issues in practical applications for rebar segmentation tasks:  
1. The segmentation of rebar edges is different, resulting in missed detections in local areas of rebars;
2. The identification of the rebar intersections and discontinuous segmentation is incomplete;
3. Due to the effects of the background and lighting, there are some instances where the background is mistakenly checked as rebar. Therefore, to address the aforementioned issues, this study made targeted improvements to the network structure of the DeeplabV3+ model. It adopted ResNet50 as the backbone network for the enhanced model and incorporated an attention mechanism within ResNet50 to handle high-semantic-information feature layers. Simultaneously, the dilation rates of the ASPP module were reduced to allow the model to more accurately capture information from targets of different sizes. The rebar segmentation configuration obtained from the improved model was further processed using image processing techniques, such as edge contour detection, to obtain the diameter and spacing of the rebars. The field test results showed that the improved DeeplabV3+ model could accurately identify the dimensional information of the rebar in line with acceptance criteria. The overall structure of the study takes the form of six sections, including this introductory section. Section 2 is the methodology section, primarily introducing the foundational model DeeplabV3+ and analyzing its existing issues in detail. It also focuses on providing a comprehensive introduction to the improved DeeplabV3+ model. Section 3 offers a comprehensive explanation of the experimental parameters and evaluation metrics employed in this study. Section 4 presents experimental results and analyses, validating the effectiveness of the improvements through a comparative analysis of the results. Section 5 delves into the aspect of rebar size measurement. The final section, Section 6, provides a conclusive summary of this paper, outlining the limitations of this study and suggesting directions for future research.