Dynamic Label Injection for Imbalanced Industrial Defect Segmentation
This addresses the problem of class imbalance in industrial defect segmentation for manufacturing quality control, representing an incremental improvement over existing methods.
The paper tackles imbalanced multi-class semantic segmentation in industrial defect detection by proposing a Dynamic Label Injection algorithm that re-balances batch defect distributions using Poisson-based seamless image cloning and cut-paste techniques, achieving better results on the Magnetic Tiles dataset compared to other balancing loss approaches, including in weakly-supervised setups.
In this work, we propose a simple yet effective method to tackle the problem of imbalanced multi-class semantic segmentation in deep learning systems. One of the key properties for a good training set is the balancing among the classes. When the input distribution is heavily imbalanced in the number of instances, the learning process could be hindered or difficult to carry on. To this end, we propose a Dynamic Label Injection (DLI) algorithm to impose a uniform distribution in the input batch. Our algorithm computes the current batch defect distribution and re-balances it by transferring defects using a combination of Poisson-based seamless image cloning and cut-paste techniques. A thorough experimental section on the Magnetic Tiles dataset shows better results of DLI compared to other balancing loss approaches also in the challenging weakly-supervised setup. The code is available at https://github.com/covisionlab/dynamic-label-injection.git