Dynamic Label Injection for Imbalanced Industrial Defect Segmentation
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
Code (1)
Tasks
Semantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data
The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will wa…
Data AugmentationDefect DetectionTransfer LearningEnhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control
Visual defect detection in industrial glass manufacturing remains a critical challenge due to the low frequency of defective products, leading to imbalanced datasets that limit the performance of deep learning models and…
Data AugmentationDefect DetectionDenoisingimage-classification+2CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for thes…
Defect DetectionTowards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain data, often struggle to capture category-…
Anomaly DetectionTL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
Surface defect detection plays an increasingly important role in manufacturing industry to guarantee the product quality. Many deep learning methods have been widely used in surface defect detection tasks, and have been …
Defect DetectionTransfer Learning