# Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
## 3 Results
### Effects of Negative Classes  
First, we only investigated the positive samples to test the performance of the model. The model recognized symbols in the test diagrams but could not distinguish dummy images from the proposed region. This demonstrates that the model, which is only trained with positive samples, can distinguish only symbols. We could observe that both PN models-k-means and DAC were superior to the P model in terms of target-symbol classification from the proposed regions. In Figure 8a, the symbols were well recognized by the P model, but dummies were also detected in the results. This means that the model, which was trained only on positive data, had a weakness in identifying negative samples as false. In contrast, PN models such as Figure 8b exhibited strong discriminative performance between symbols and dummies. The dummies confused with the check valves and gate valves were filtered out by the PN models. These results indicated that the assignment of a negative class for classification gives the model the ability to effectively identify symbols from the pool of binary component images.  
We verified this statement in Table 2. In terms of SR--the extent to which the model could recognize the symbols in the diagram--all the models delivered good performance of over 96%. The PN_DAC model outperformed the other two, with 98.08%. This suggests that in the PN models, there was enhanced ability to classify targets through the assignment of a negative class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **SR (\%)** & **DR (\%)** \\ \hline P Model & 96.97 & 42.31 \\ PN\_Kmeans Model & 97.88 & 1.35 \\ PN\_DAC Model & 98.08 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the models.  
Figure 8: Sample results from the (**a**) P_model, (**b**) PN_Kmeans model.  
DR demonstrated the remarkable ability of both PN models. In the P model, 42.3% of the dummy images from the test diagrams were confused with our target symbols. This meant that the P model was incapable of identifying what images represented a genuine symbol. Due to the characteristics of EDs, i.e., their binary representation, it was difficult for the model to recognize them. In the latent space of the convolutional network, the latent features of both the dummies and the symbols were confused with each other under the P model because it does not possess any information concerning negative images. Hence, we conclude that negative classes are required for object-detection algorithms in EDs.  
Compared to the P model, the models that consider negative samples achieve a significant reduction in the dummy detection rate. Binary images contain only a limited amount of information. Although most images in real-world applications consist of three channels-red, green, and blue-engineering diagrams consist of one channel-grayscale. They feature only one channel, which consists of quasi-binary components; hence, there is limited information available in the image, such as local features or pixel intensity. In this respect, we can say that for engineering diagrams to effectively recognize plant symbols and discard the detected dummies from the proposed regions, a dataset containing positive and negative classes is required. Consideration of the negative results yields the additional model information through which candidates can be assessed effectively.