Accessibility navigation


Transfer learning for instance segmentation of waste bottles using Mask R-CNN algorithm

Jaikumar, P., Vandaele, R. and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2021) Transfer learning for instance segmentation of waste bottles using Mask R-CNN algorithm. In: International Conference on Intelligent Systems Design and Applications, 12-15 December 2020, https://link.springer.com/chapter/10.1007%2F978-3-030-71187-0_13, pp. 140-149, https://doi.org/10.1007/978-3-030-71187-0_13.

[img]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

8MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1007/978-3-030-71187-0_13

Abstract/Summary

This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the mask region proposal convolutional neural network (Mask R-CNN). Plastic bottles constitute one of the major pollutants posing a serious threat to the environment both in oceans and on land. The automated identification and segregation of bottles can facilitate plastic waste recycling. We prepare a custom-made dataset of 192 bottle images with pixel-by pixel-polygon annotation for the automatic segmentation task. The proposed transfer learning scheme makes use of a Mask R-CNN model pre-trained on the Microsoft COCO dataset. We present a comprehensive scheme for fine-tuning the base pre-trained Mask-RCNN model on our custom dataset. Our final fine-tuned model has achieved 59.4 mean average precision (mAP), which corresponds to the MS COCO metric. The results indicate promising application of deep learning for detecting waste bottles.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:98569

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation