Multiple view anomaly detection in images from UAS structure inspection using CNNs

2019 ◽  
Author(s):  
Paul Debus ◽  
Christian Benz ◽  
Volker Rodehorst
Keyword(s):  
Anomaly Detection ◽  
Unmanned Aircraft ◽  
Data Sets ◽  
Great Success ◽  
Imaging Geometry ◽  
Large Structures ◽  
3D Geometry ◽  
Novel Method ◽  
Aircraft System ◽  
Rich Data

<p>A novel method for automated anomaly detection in images acquired in structure inspection based on unmanned aircraft system (UAS) by means of deep learning is proposed. Using UAS in the inspection of large structures, rich data sets are produced, that can be used to support human inspectors. The image positions and orientations can automatically be reconstructed using structure from motion (SfM). A photogrammetric reconstruction of the 3D geometry is an established method for the analysis of deformations of structures. On this basis, a convolutional neural network (CNN) can be used to detect anomalies, such as cracks in the acquired images. While recently CNNs have been implemented with great success, the detection can further be improved by fusing the obtained results using geometry information gathered from photogrammetric reconstruction. The method leverages the imaging geometry reconstructed using SfM to significantly reduce the error rate of the network. The proposed method applies a fusion mechanism on detected anomalies in adjacent images to improve the detection performance.</p>

scholarly journals Drone-Based Vibration Monitoring and Assessment of Structures

2021 ◽  
Vol 11 (18) ◽  
pp. 8560
Author(s):  
Sabrina Carroll ◽  
Joud Satme ◽  
Shadhan Alkharusi ◽  
Nikolaos Vitzilaios ◽  
Austin Downey ◽  
...  
Keyword(s):  
Impulse Response Function ◽  
Unmanned Aircraft ◽  
Vibration Monitoring ◽  
The Novel ◽  
Civil Structures ◽  
External Power ◽  
Novel Method ◽  
Aircraft System ◽  
Sensor Package ◽  
Dynamic Signature

This paper presents a novel method of procuring and processing data for the assessment of civil structures via vibration monitoring. This includes the development of a custom sensor package designed to minimize the size/weight while being fully self-sufficient (i.e., not relying on external power). The developed package is delivered to the structure utilizing a customized Unmanned Aircraft System (UAS), otherwise known as a drone. The sensor package features an electropermanent magnet for securing it to the civil structure while a second magnet is used to secure the package to the drone during flight. The novel B-Spline Impulse Response Function (BIRF) technique was utilized to extract the Dynamic Signature Response (DSR) from the data collected by the sensor package. Experimental results are presented to validate this method and show the feasibility of deploying the sensor package on structures and collecting data valuable for Structural Health Monitoring (SHM) data processing. The advantages and limitations of the proposed techniques are discussed, and recommendations for further developments are made.

Review of Unmanned Aircraft System (UAS)

2013 ◽  
Author(s):  
Suraj G. Gupta ◽  
Mangesh Ghonge ◽  
Pradip M. Jawandhiya
Keyword(s):  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Aircraft System

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

Sign in / Sign up

Export Citation Format

Share Document