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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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Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety-critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). This work introduces the Risk Ranked Recall ($R^3$) metrics for object detection systems. The $R^3$ metrics categorize objects within three ranks. Ranks are assigned based on an objective cyber-physical model for the risk of collision. Recall is measured for each rank
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- Award ID(s):
- 1932529
- PAR ID:
- 10296832
- Date Published:
- Journal Name:
- 10th Mediterranean Conference on Embedded Computing
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Access to electricity positively correlates with many beneficial socioeconomic outcomes in the developing world including improvements in education, health, and poverty. Efficient planning for electricity access requires information on the location of existing electric transmission and distribution infrastructure; however, the data on existing infrastructure is often unavailable or expensive. We propose a deep learning based method to automatically detect electric transmission infrastructure from aerial imagery and quantify those results with traditional object detection performance metrics. In addition, we explore two challenges to applying these techniques at scale: (1) how models trained on particular geographies generalize to other locations and (2) how the spatial resolution of imagery impacts infrastructure detection accuracy. Our approach results in object detection performance with an F1 score of 0.53 (0.47 precision and 0.60 recall). Using training data that includes more diverse geographies improves performance across the 4 geographies that we examined. Image resolution significantly impacts object detection performance and decreases precipitously as the image resolution decreases.more » « less
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Abstract—Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of 0.69m and 0.33m for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of 3 − 4Hz.more » « less
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Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to emergency response authorities in real-time.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MECO52532.2021.9460196
