Data-centric Approach for Instance Segmentation in Optical Waste Sorting

Computer vision systems have been integrated into facilities dealing with the sorting of household waste. This solution allows for the sorting efficiency improvement and cost reduction. However, challenges associated with the poor annotation quality of existing waste segmentation datasets, unsuitable environment for recognition on a conveyor belt, or limited data for creating an effective and cost-efficient sorting system using visible range cameras significantly limit the application efficiency of computer vision systems. In this article, we report on the data-centric pipeline for enhancing the precision of predictions in multiclass household waste segmentation on a conveyor belt. In particular, we have demonstrated that by employing a pseudo-annotation approach combined with an object-based data augmentation algorithm, it is possible to train a model on a set of 'simple' images and achieve satisfactory results when estimating the model on a set of 'complex' images. We collected and prepared the dataset consisting of 5 k manually labeled data and additionally 10 k pseudo-labeled data by object-based augmentation. The proposed pipeline incorporates data balancing, transfer learning, and pseudo-labeling to improve the mean Average Precision (mAP) of the YOLOV8 segmentation model from 67 % to 83 % for 'simple' use case scenarios and from 42 % to 59 % or 'complex' industrial solutions.