Journal
SENSORS
Volume 20, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/s20061562
Keywords
surface defect detection; convolutional neural network; object detection
Funding
- National key research and development plan [2017YFF0204800]
- National Natural Science Foundations of China [61971307, 61905175, 51775377]
- National Science and Technology Major Project [2017-V-0009]
- Tianjin Natural Science Foundations of China [17JCQNJC01100]
- Fok Ying Tung education foundation [171055]
- Science and Technology on Underwater Information and Control Laboratory [6142218081811]
- Equipment pre-research field fund [61405180505, 61400040303]
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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.
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