5,751 research outputs found
Towards Accurate Camera Geopositioning by Image Matching
In this work, we present a camera geopositioning system based on matching a
query image against a database with panoramic images. For matching, our system
uses memory vectors aggregated from global image descriptors based on
convolutional features to facilitate fast searching in the database. To speed
up searching, a clustering algorithm is used to balance geographical
positioning and computation time. We refine the obtained position from the
query image using a new outlier removal algorithm. The matching of the query
image is obtained with a recall@5 larger than 90% for panorama-to-panorama
matching. We cluster available panoramas from geographically adjacent locations
into a single compact representation and observe computational gains of
approximately 50% at the cost of only a small (approximately 3%) recall loss.
Finally, we present a coordinate estimation algorithm that reduces the median
geopositioning error by up to 20%
Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance
Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities.
In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment.
Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities.
Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project
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