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Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data. [ABSTRACT FROM AUTHOR]

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection. [ABSTRACT FROM AUTHOR]

We propose a synthetic image method named local-foreground generative adversarial networks for surface defect detection in the industry. The method comprises three contributions: First, in order to provide more training data, an algorithm that generates full synthetic images of defects is proposed. The method may blend the defect samples stored in the different mobile terminal into existing background images of the production environment in a natural way, accounting for both geometry and appearance. Second, the diversity of defects appearance is increased due to the use of deep convolutional generative adversarial networks only for local-foreground and the defect edge details that are preserved by introducing a guided filter in the process of image synthesis. Third, the image fusion method can adapt to the various production environment of different brightness and camera angles, which has strong adaptability and expansion ability. We also discuss the experimental results of synthetic data on an end-to-end detection system based on deep learning. As we know, YOLO detector trained on synthetic data achieving average precision of 95.2% on test dataset, which is 12.5% higher than heuristic data augmentation individually. Furthermore, it can process 23 images / s on 1080Ti GPU, which meets the requirements for real-time industrial inspection. [ABSTRACT FROM AUTHOR]