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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.

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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:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:98569

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