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Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site.
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Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
e apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with ∼30 galaxies/arcmin2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn
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- Award ID(s):
- 1725729
- PAR ID:
- 10121464
- Date Published:
- Journal Name:
- Monthly notices of the Royal Astronomical Society
- ISSN:
- 0035-8711
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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