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Transfer Learning for Instance Segmentation of Waste Bottles Using Mask R-CNN Algorithm

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

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 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), that corresponds to the MS COCO metric. The results indicate promising application of deep leaning for detecting waste bottles.

Keywords

  • Convolutional neural networks
  • Object detection
  • Instance segmentation
  • Deep learning
  • Transfer learning

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Correspondence to Varun Ojha .

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Jaikumar, P., Vandaele, R., Ojha, V. (2021). Transfer Learning for Instance Segmentation of Waste Bottles Using Mask R-CNN Algorithm. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_13

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