<p>Piping is an essential component in buildings,
and its as-built information is critical to facility management tasks. Manually
extracting piping information from legacy drawings that are in paper, PDF, or
image format is mentally exerting, time-consuming, and error-prone. Symbol
recognition and detection are core problems in the computer-based
interpretation of piping drawings, and the main technical challenge is to
determine robust features that are invariant to scaling, rotation, and
translation. This thesis aims to use convolutional neural networks (CNNs) to
automatically extract features from raw images, and consequently, to locate and
recognize symbols in piping drawings.</p>
<p>In this thesis, the Spatial Transformer
Network (STN) is applied to improve the performance of a standard CNN model for
recognizing piping symbols, and the Faster Region-based Convolutional Neural
Network (Faster RCNN) is adopted to exploit its capacity in symbol detection.
For experimentation, the synthetic data are generated as follows. Two datasets
are generated for symbol recognition and detection, respectively. For
recognition, eight types of symbols are synthesized based on the geometric
constraints between the primitives. The drawing samples for detection are
manually sketched using AutoCAD MEP software and its piping component library,
and seven types of symbols are selected from the piping component library. Both
sets of samples are augmented with various scales, rotations, and random
noises.</p>
<p>The experiment
for symbol recognition is conducted and the accuracies of the recognition
accuracy of the CNN + STN model and the standard CNN model are compared. It is observed
that the spatial transformer layer improves the accuracy in classifying piping
symbols from 95.39% to 98.26%. For the symbol detection task, the experiment is
conducted using a public implementation of Faster RCNN. The mean Average
Precision (mAP) is 82.8% when Intersection over Union (IoU) threshold equals to
0.5. Imbalanced data (i.e., imbalanced samples in each class) led to a decrease
in the Average Precision in the minority class. Also, the symbol library, the
small dataset, and the complex backbone network limit the generality of the
model. Future work will focus on the
collection of larger set of drawings and the improvement of the network’s
geometric invariance.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8301080 |
| Date | 16 August 2019 |
| Creators | Yuxi Zhang (6861506) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/CNN-based_Symbol_Recognition_and_Detection_in_Piping_Drawings/8301080 |
Page generated in 0.002 seconds