Abstract
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.
Highlights
Surface defects have a greatly adverse effect on the quality of industrial products
We propose an end-to-end metallic surface defect network based on the Single Shot
The data collection system consists of a set of linear array CCD cameras with a direct current (DC) light source to avoid the presence of stripes produced by an alternating current (AC)
Summary
Surface defects have a greatly adverse effect on the quality of industrial products. Metallic defects detection has been exploited to satisfy predefined quality requirements for the industry. Metallic surface defect detection is influenced by many environmental factors such as illumination, light reflection, and metal material. These factors significantly increase the difficulty of surface defect detection. The dataset size is generally limited to several hundred, which may lead to a detection model with weak robustness and generalization under complex industrial scenarios. To solve such a problem, it is necessary to introduce a new benchmark that is closer to realistic scenarios. We construct a new metallic surface defect dataset, named the “GC10-DET”
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