# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 6 Concluding Remarks  
To address the challenge of rebar size detection in construction quality acceptance, an improved DeeplabV3+ model combined with image processing techniques was proposed for the fast identification and measurement of rebar. ResNet50 was used as the backbone network instead of Xception for multi-scale feature fusion. The dilation rate and density of the atrous convolution in the ASPP was adjusted, and an attention mechanism was added to obtain a rebar segmentation configuration with high accuracy. The recognized segmentation configuration was then used for image processing, such as edge detection, to obtain an accurate measurement of the rebar's size and spacing. In comparison with the original DeeplabV3+ model, our proposed model improved the mIoU, precision, recall, and F1 score by 4.96%, 0.22%, 5.04%, and 2.70%, respectively, and the number of parameters was reduced from 42.1 M to 27.4 M. Moreover, in comparison with other algorithms, our proposed model had higher detection accuracy and shorter inference time for a single image. Furthermore, the diameter and spacing detection accuracy of steel rebars reached the standard of acceptance; thus, it can be effectively applied for the intelligent detection of steel rebars. However, our research method also has certain limitations. The images in this research dataset were collected under clear or dry weather conditions, without considering the interference caused by rainy weather, which would result in wet backgrounds on the template that could lead to reflections, as well as variations in lighting at different times. When applied in situations of excessive brightness or darkness, as well as backgrounds with wet reflections, this method led to significant errors in segmenting rebar images. In future studies, we plan to expand the dataset by capturing steel images in different weather conditions and at various times. We will also use data augmentation algorithms to enhance image features, mitigate the impact of weather and lighting factors, and improve the model's robustness and generalization capabilities. Conceptualization, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]), X.F. and Z.P. ; formal analysis, [PERSON] ([PERSON]), X.F. and [PERSON]. ([PERSON]); funding acquisition, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]), X.F. and [PERSON]. ([PERSON]); project administration, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]) and X.F. ; supervision, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and X.F. ; writing--original draft, [PERSON] ([PERSON]), X.F. and Z.P. ; writing--review and editing, [PERSON] ([PERSON]), X.F. and [PERSON]. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Natural Science Foundation of China under Grant 51408063, author [PERSON]. ([PERSON]); the Changsha University of Science and Technology Innovative Project of Civil Engineering Excellent Characteristic Key Discipline 16 ZDXK06, author \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Real Spacing /mm** & **Model** & **Test Results /mm** & **Error /mm** & **Qualified** **or Not** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{203.5} & DeeplabV3+ & 184.4 & \(-\)19.1 & No \\  & & Ours & 196.2 & \(-\)7.3 & Yes \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{202.4} & DeeplabV3+ & 186.2 & \(-\)16.2 & No \\  & & Ours & 199.6 & \(-\)2.8 & Yes \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{201.9} & DeeplabV3+ & 181.4 & \(-\)15.5 & No \\  & & Ours & 199.3 & \(-\)5.6 & Yes \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{201.6} & DeeplabV3+ & 183.1 & \(-\)18.5 & No \\  & & Ours & 195.0 & \(-\)6.6 & Yes \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{206.8} & DeeplabV3+ & 184.4 & \(-\)12.4 & No \\  & & Ours & 199.4 & \(-\)7.4 & Yes \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{202.3} & DeeplabV3+ & 188.1 & \(-\)14.2 & No \\  & & Ours & 197.2 & \(-\)5.1 & Yes \\ \hline \hline \end{tabular}
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