TY  - CONF
Y1  - 2022///
SN  - 9798350397000
PB  - Institute of Electrical and Electronics Engineers Inc.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147002843&doi=10.1109%2fICDI57181.2022.10007281&partnerID=40&md5=92533310bfdb0e859ca451723d3db275
A1  - Bhanbhro, H.
A1  - Hooi, Y.K.
A1  - Hassan, Z.
A1  - Sohu, N.
EP  - 126
AV  - none
N1  - cited By 2; Conference of 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:185994
N2  - Due to the rapid advances in object detection methods, object recognition in the engineering drawings has gained researchers' attention in recent years. Usually, engineering drawings are analysed using traditional object detection methods. However, complex structures and their connections sometimes make it challenging for the traditional object detection architectures to process the engineering drawings. With the rapid development in deep learning models, more powerful methods-which are able to interpret high-level and complex features-are introduced to address the challenges exhibited by the traditional methods. These models have different training strategies and thus portray varied behaviors. In this paper, we present a comparative analysis of the deep learning models for object recognition in engineering drawings. Furthermore, real-life scenario on how to process and analyses information from specific engineering drawings, namely, piping and instrumentation diagrams (P&IDs) is discussed in details. Finally, future directions are presented which can serve as research guidelines to work in this domain. © 2022 IEEE.
KW  - Deep learning; Engineering education; Engineering research; Object detection; Signal detection
KW  -  Complex engineering; Complexes structure; Deep learning; Engineering drawing; Learning approach; Learning models; Object detection method; Objects detection; Objects recognition; Symbols detection
KW  -  Object recognition
TI  - Modern Deep Learning Approaches for Symbol Detection in Complex Engineering Drawings
SP  - 121
ID  - scholars17293
ER  -