Skip to content

Leveraging an instance segmentation method for detection of transparent materials

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Automatic detection of transparent materials (e.g., glass, plastic, etc.) is essential in many computer vision tasks. For example, a robot could use such a system to navigate around transmissive materials or operate tasks with these materials without causing damage. Nevertheless, it is challenging task as such materials exhibit less texture or background scenes dominate visual perception. Existing methods used either handengineered or leaned features to detect and segment transparent objects. We argue that pixel-wise detection and segmentation of transmissive materials improve detection performance and provide the fine-grained information compared to detecting bounding boxes of objects (i.e., localisation task). In this paper, we leverage a robust and state-of-the-art instance segmentation method namely, Mask R-CNN, in order to detect transparent materials. To be specific, we train the model on a new dataset with an evaluation based on publicly available dataset. Experimental results show that the adopted method significantly enhances the performance of transparent material detection. In particular, the resulting binary masks provides the pixel-level information for an improved understanding and analysis of transparency.
Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)978-1-7281-4034-6
ISBN (Print)978-1-7281-4035-3
DOIs
Publication statusPublished - 9 Apr 2020
Event2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation - Leicester, United Kingdom
Duration: 19 Aug 201923 Aug 2019

Conference

Conference2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
Abbreviated title(SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
CountryUnited Kingdom
CityLeicester
Period19/08/1923/08/19

Documents

  • Leveraging an Instance Segmentation Method for Detection of Transparent Materials_pp

    Rights statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript (Post-print), 2.7 MB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 20886034