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High helicopter accident rate
General Aviation (GA) and small helicopter have low participation rate in the flight data monitoring programs (e.g. FDR)
=> video data analysis based approach is proposed
Objective: to develop an intelligent video data analysis tool for flight data monitoring
Flight state information inference for basic flight parameters that most FDRs are required to record
Attitude Estimation from Outside View
Attitude Estimation from Outside View
Using DBSCAN, core points are extracted that can
reduce computational cost
deal with significant camera’s movement
deal with noises (e.g. light)
Analogue Gauge Reading
Analogue Gauge Reading
Using DBSCAN & threshold-based filter
Can reduce computational cost using only core points
Can deal with various layouts of flight instrument panels by automatically detect the positions of analogue gauges
Digital Display Reading
Digital Display Reading
Digital Number Indicators
Digital Number Indicators
Original images of digital numbers
Feature Selection: HOG (Histogram of Oriented Gradients)
Classification: Support Vector Machines (SVM) => Precision: 92.5%
Primary Flight Display (PFD)
Primary Flight Display (PFD)
Hough transform-based line detection
Electronic Attitude Display System (EADS)
Red line in the right video: detected horizon
Precision: 91.2%
Detected roll angle
Detected pitch angle
Alarms Sound Detection
Alarms Sound Detection
Single alarm detection:
Short Time Fourier Transform (STFT) => type and occurrence time detection
Multiple alarm detection: integrate STFT with
Statistical evaluation to find relationships between two data:
Change detection algorithm (CUSUM) to detect small changes in values of signal
Five alarm mixed sound
Autopilot disco: 2.12 – 11.68 sec
Dash outer: 7.49 – 9.83 sec
Dot inner: 9.24 – 13.74 sec
Dash middle: 8.03 – 17.36 sec
Stall: 11.86 – 13.75 sec
Engine Parameter Estimation
Engine Parameter Estimation
Using statistical model and DBSCAN
Can deal with various noises by efficiently identifying dominant frequency correlated with flight parameter
Can estimate the engine parameters using a statistical model without sound profiles
Correlation between Flight Parameters and Noise
Statistical model created using audio from different flights
Flight parameters can be estimated by frequency analysis and the statistical model
Related Publications
Related Publications
S. Shin and I. Hwang, “Data Mining Based Computer Vision Analytics for Automated Helicopter Flight State Inference,” AIAA Journal of Aerospace Information Systems, (submitted on November 19, 2016; 1st revision submitted on May 15, 2017; Accepted on September 6, 2017)
S. Shin and I. Hwang, “Helicopter Cockpit Audio Data Analysis to Infer Flight State Information,” Journal of the American Helicopter Society, (submitted on November 19, 2016; under review)
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