relation: https://khub.utp.edu.my/scholars/17293/
title: Modern Deep Learning Approaches for Symbol Detection in Complex Engineering Drawings
creator: Bhanbhro, H.
creator: Hooi, Y.K.
creator: Hassan, Z.
creator: Sohu, N.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Bhanbhro, H. and Hooi, Y.K. and Hassan, Z. and Sohu, N.  (2022) Modern Deep Learning Approaches for Symbol Detection in Complex Engineering Drawings.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147002843&doi=10.1109%2fICDI57181.2022.10007281&partnerID=40&md5=92533310bfdb0e859ca451723d3db275
relation: 10.1109/ICDI57181.2022.10007281
identifier: 10.1109/ICDI57181.2022.10007281