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1. Detecting Cracks and Spalling Automatically in Extreme Events by End-to-end Deep Learning Frameworks

   https://doi.org/https://doi.org/10.5194/isprs-annals-V-2-2021-161-2021  

   Bai M., Sezen H. (January 2021, ISPRS Annals of Photogrammetry and Remote Sensing Spatial Information Science, XXIV ISPRS Congress, International Society for Photogrammetry and Remote Sensing)

   In this paper, we develop and implement end-to-end deep learning approaches to automatically detect two important types of structural failures, cracks and spalling, of buildings and bridges in extreme events such as major earthquakes. A total of 2,229 images were annotated, and are used to train and validate three newly developed Mask Regional Convolutional Neural Networks (Mask R-CNNs). In addition, three sets of public images for different disasters were used to test the accuracy of these models. For detecting and marking these two types of structural failures, one of proposed methods can achieve an accuracy of 67.6% and 81.1%, respectively, on low- and high-resolution images collected from field investigations. The results demonstrate that it is feasible to use the proposed end-to-end method for automatically locating and segmenting the damage using 2D images which can help human experts in cases of disasters. 

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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. Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

   https://doi.org/10.1002/lom3.10591  

   Bi, Hongsheng; Cheng, Yunhao; Cheng, Xuemin; Benfield, Mark C; Kimmel, David G; Zheng, Haiyong; Groves, Sabrina; Ying, Kezhen (January 2024, Limnology and Oceanography: Methods)

   Abstract Underwater imaging enables nondestructive plankton sampling at frequencies, durations, and resolutions unattainable by traditional methods. These systems necessitate automated processes to identify organisms efficiently. Early underwater image processing used a standard approach: binarizing images to segment targets, then integrating deep learning models for classification. While intuitive, this infrastructure has limitations in handling high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and highly variable target sizes. To address these challenges, we introduce a new framework that starts with a scene classifier to capture large within‐image variation, such as disparities in the layout of particles and dominant taxa. After scene classification, scene‐specific Mask regional convolutional neural network (Mask R‐CNN) models are trained to separate target objects into different groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal plankton images, we compared the scene‐specific models to the Mask R‐CNN model encompassing all scene categories as a single full model. Results showed that the scene‐specific approach outperformed the full model by achieving a 20% accuracy improvement in complex noisy images. The full model yielded counts that were up to 78% lower than those enumerated by the scene‐specific model for some small‐sized plankton groups. We further tested the framework on images from a benthic video camera and an imaging sonar system with good results. The integration of scene classification, which groups similar images together, can improve the accuracy of detection and classification for complex marine biological images. 

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4. Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

   https://doi.org/10.1145/3576842.3582366  

   Sandha, Sandeep Singh; Balaji, Bharathan; Garcia, Luis; Srivastava, Mani (May 2023, IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation)

   Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle1 to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios2. 

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5. 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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