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, online, pp. 140-149. doi: 10.1007/978-3-030-71187-0_13 (Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351.)

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)
URI	https://reading-clone.eprints-hosting.org/id/eprint/98569
Identification Number/DOI	10.1007/978-3-030-71187-0_13
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
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