| Summary: | Since tiny optical glass is the key component in various optical instruments, more and more researchers have paid attention to automatic defect detection on tiny optical glass in recent years. It remains a challenging problem, as the defects are extremely small. In this paper, we propose a video-based two-stage defect detection network to improve detection accuracy for small defects. Specifically, the detection process is carried out in a coarse-to-fine manner to improve the detection precision. First, the optical glass area is located on the down-sampled version of the input image, and then defects are detected only within the optical glass area with a higher resolution version, which can significantly reduce the false alarming rate. Since the defects may exist on any place of the optical glass, we fuse the results of multiple video frames captured from various perspectives to promote recall rates of the defects. Additionally, we propose an image quality evaluation module based on a clustering algorithm to select video frames with high quality for improving both detection recall and precision. We contribute a new dataset called OGD-DET for tiny-scale optical glass surface defect detection experiments. The datasets consist of 3415 images from 40 videos, and the size of the defect area ranges from 0.1 mm to 0.53 mm, 2 to 7 pixels on images with a resolution of 1536 × 1024 pixels. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in terms of both accuracy and computation cost.
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