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

TL;DR

This paper introduces a transfer learning approach using Mask R-CNN for detecting and segmenting plastic waste bottles, achieving a 59.4 mAP on a custom dataset to aid recycling efforts.

Contribution

It presents a novel transfer learning scheme with fine-tuning of Mask R-CNN on a custom dataset for waste bottle segmentation.

Findings

Achieved 59.4 mAP on custom dataset

Demonstrated effective transfer learning for waste detection

Facilitated potential recycling automation

Abstract

This paper proposes a methodological approach with a transfer learning scheme for plastic waste bottle detection and instance segmentation using the \textit{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 \textit{mean average precision} (mAP), which corresponds to the MS COCO metric. The results indicate a promising application of deep learning for detecting waste bottles.