Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning
This dataset and methodology are significant for researchers developing machine learning models for automatic defect detection in apples, by providing well-structured data and a solution to the common problem of label imbalance.
This paper introduces three parallel-beam X-ray CT datasets of 94 apples with internal defects, designed for machine learning tasks like image reconstruction and segmentation. The authors also address the natural label bias in defect datasets by formulating it as an optimization problem and providing two methods (heuristic and mixed integer quadratic programming) to eliminate this bias when splitting the data.
We present three parallel-beam tomographic datasets of 94 apples with internal defects along with defect label files. The datasets are prepared for development and testing of data-driven, learning-based image reconstruction, segmentation and post-processing methods. The three versions are a noiseless simulation; simulation with added Gaussian noise, and with scattering noise. The datasets are based on real 3D X-ray CT data and their subsequent volume reconstructions. The ground truth images, based on the volume reconstructions, are also available through this project. Apples contain various defects, which naturally introduce a label bias. We tackle this by formulating the bias as an optimization problem. In addition, we demonstrate solving this problem with two methods: a simple heuristic algorithm and through mixed integer quadratic programming. This ensures the datasets can be split into test, training or validation subsets with the label bias eliminated. Therefore the datasets can be used for image reconstruction, segmentation, automatic defect detection, and testing the effects of (as well as applying new methodologies for removing) label bias in machine learning.