# Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
## 4 Conclusions  
In this study, an R-CNN for engineering diagrams was proposed, taking negative classes into account. For an RPN, sequential image processing was modified for each target--value and instruments. To annotate the negative class for the dummy images, two unsupervised learning algorithms-k-means and DAC-were applied to decompose the hidden patterns of the dummies, and assign negative classes. A simple convolutional network was used as the classification model because of its superior characteristics in terms of local information extraction from the images.  
There were three types of datasets used for the classification problem--positive (P model), positive with negative through k-means (PN-Kmeans model), and positive with negative through DAC (PN-DAC model). Compared to the P model, both k-means and DAC had relatively low dummy detection rates of 1.35% and 0.39%, respectively, because the negative class from the unsupervised algorithm improved the model's ability to distinguish dummies from the symbols in the diagrams. Moreover, DAC was a superior algorithm for decomposing binary representations, as the PN-DAC model had superior performance, in which the symbol recognition rate (SR) and the dummy detection rate (DR) were 98.08% and 0.39 %, respectively.  
From these results, we can verify that the proposed model meets the applicability and practicality criteria for P&ID object detection algorithms. The algorithm's negative sample detection reduces due to dummies, which makes its application to real projects difficult. This object detection algorithm is expected to contribute to the automatic digitalization of engineering diagrams. Regarding further work, state-of-the-art algorithms for object detection, such as Faster R-CNN, You Only Look Once(YOLO)_v3, and Single Shot Multi-Box Detector (SSD), could be modified to suit engineering diagrams. For real-world applications, a tiny-object detector also would be useful as a plant symbol recognition model.  
Conceptualization, D.-Y.Y.; methodology, D.-Y.Y.; software, D.-Y.Y. and S.-K.S.; validation, D.-Y.Y. and S.-K.S.; formal analysis, D.-Y.Y. and S.-K.S.; investigation, D.-Y.Y.; resources, D.-Y.Y.; data curation, D.-Y.Y.; writing--original draft preparation, D.-Y.Y. and S.-K.S.; writing--review and editing, D.-Y.Y. and S.-K.S.; visualization, D.-Y.Y.; supervision, C.-J.L. and U.Z.; project administration, C.-J.L. and U.Z.; funding acquisition, C.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Chung-Ang University Research Grants in 2018 and Seoul R&BD Program (20191471).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **F1 Score** \\ \hline
3 way\_Vlv & 0.89 \\ Ball\_Vlv & 1.00 \\ Gate\_Vlv & 0.97 \\ Butterfly\_Vlv & 0.95 \\ Check\_Vlv & 0.85 \\ Relief\_Vlv & 0.75 \\ Globe\_Vlv & 0.93 \\ Utility & 0.90 \\ Sensor & 1.00 \\ PLC & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1 score for each class (PN_DAC).