# Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm Source: [Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:08:48.414Z Authors: Punitha Jaikumar, Remy Vandaele, Varun Ojha Published: 2022-04-15T12:19:24.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()