论文标题
转移学习，例如使用Mask R-CNN算法对废瓶进行分割
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm
论文作者
论文摘要
本文提出了一种方法学方法，该方法是使用\ textIt {mask区域建议卷积神经网络}（mask r-CNN）的转移学习方案进行转移学习方案和实例分割。塑料瓶构成了对海洋和陆地上环境构成严重威胁的主要污染物之一。瓶子的自动识别和分离可以促进塑料废物回收。我们为自动分割任务的像素式式批评编制了192个瓶子图像的定制数据集。提出的转移学习方案利用在Microsoft可可数据集上预先训练的蒙版R-CNN模型。我们提出了一个综合计划，用于微调我们自定义数据集中的基本预训练的蒙版模型。我们的最终微调模型已达到59.4 \ textit {平均平均精度}（MAP），该模型与MS Coco Metric相对应。结果表明，深度学习可用于检测废物瓶。
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.
