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1. Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning

   Colin J Burke, Patrick D (October 2019, Monthly notices of the Royal Astronomical Society)

   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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2. Deep Learning-Based Crack Detection Using Mask R-CNN Technique

   Tan, Chenjun; Uddin, Nasim; Mohammed, Yahya M. (August 2019, 9 th International Conference on Structural Health Monitoring of Intelligent Infrastructure)

   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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3. Instance Segmentation with Mask-supervised Polygonal Regression Transformers

   Lazarow, Justin; Xu, Weijian; Tu, Zhuowen (June 2022, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance. This sparse, vectorized boundary representation for objects, while attractive in many downstream computer vision tasks, quickly runs into issues of parity that need to be addressed: parity in supervision and parity in performance when compared to existing pixel-based methods. This is due in part to object instances being annotated with ground-truth in the form of polygonal boundaries or segmentation masks, yet being evaluated in a convenient manner using only segmentation masks. Our method, BoundaryFormer, is a Transformer based architecture that directly predicts polygons yet uses instance mask segmentations as the ground-truth supervision for computing the loss. We achieve this by developing an end-to-end differentiable model that solely relies on supervision within the mask space through differentiable rasterization. BoundaryFormer matches or surpasses the Mask R-CNN method in terms of instance segmentation quality on both COCO and Cityscapes while exhibiting significantly better transferability across datasets. 

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4. In situ measurements of the reflectances of the LSSTCam optics and assessing the impact of optical ghosts

   https://doi.org/10.1117/12.3104153  

   Pai, Aashay; Drlica-Wagner, Alex; Kelvin, Lee S; Meyers, Joshua E; Urbach, Elana K; Mueller, Fritz; Lupton, Robert H (August 2026, SPIE)

   Roberts, Scott; Egner, Sébastien E (Ed.)

   Optical ghosts are image artifacts caused by successive reflections of light between optical surfaces such as lenses, filters, and detectors. These artifacts are unavoidable due to the nonzero reflectances of optical elements and are a major source of contamination for low-surface-brightness science. We use optical ray tracing simulations tuned to observations from LSST Commissioning to quantify the impact of optical ghosts on the LSST data. In particular, we find that ∼ 0.57% of the LSSTCam focal plane is impacted by optical ghosts when averaged across all bands. We also use data from the Collimated Beam Projector to measure the reflectances of various optical elements, generally confirming estimates of ∼2% from the systems engineering throughput predictions. 

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5. Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

   https://doi.org/10.1002/arp.1909  

   Quintus, Seth; Davis, Dylan S.; Cochrane, Ethan E. (January 2023, Archaeological Prospection)

   Lidar datasets have been crucial for documenting the scale and nature of human ecosystem engineering and land use. Automated analysis methods, which have been rising in popularity and efficiency, allow for systematic evaluations of vast landscapes. Here, we use a Mask R-CNN deep learning model to evaluate terracing – artificially flattened areas surrounded by steeper slopes – on islands in American Sāmoa. Mask R-CNN is notable for its ability to simultaneously perform detection and segmentation tasks related to object recognition, thereby providing robust datasets of both geographic locations of terracing features and their spatial morphometry. Using training datasets from across American Sāmoa, we train this model to recognize terracing features and then apply it to the island of Tutuila to undertake an island-wide survey for terrace locations, distributions, and morphologies. We demonstrate that this model is effective (F1 = 0.718), but limitations are also documented that relate to the quality of the lidar data and the size of terracing features. Our data shows that the islands of American Sāmoa display shared patterns of terracing, but the nature of these patterns are distinct on Tutuila compared to the Manuʻa island group. These patterns speak to the different interior configurations of the islands. This study demonstrates how deep learning provides a better understanding of landscape construction and behavioral patterning on Tutuila and has the capacity to expand our understanding of these processes on other islands beyond our case study. 

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