Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

International Journal of Micro Air Vehicles ◽

10.1177/1756829318822327 ◽

2019 ◽

Vol 11 ◽

pp. 175682931882232

Author(s):

Navid Dorudian ◽

Stanislao Lauria ◽

Stephen Swift

Keyword(s):

Background Subtraction ◽

High Speed ◽

Micro Air Vehicles ◽

Background Model ◽

Depth Information ◽

Subtraction Technique ◽

Depth Images ◽

Novel Approach ◽

Model Update ◽

Air Vehicles

A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

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A novel high-speed rotary ultrasonic motor applied to micro air vehicles

2016 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications (SPAWDA) ◽

10.1109/spawda.2016.7829984 ◽

2016 ◽

Cited By ~ 2

Author(s):

Le Wang ◽

Xiao-long Lu ◽

Chun-sheng Zhao ◽

Cheng Xue

Keyword(s):

High Speed ◽

Micro Air Vehicles ◽

Ultrasonic Motor ◽

Air Vehicles

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In-flight model parameter and state estimation using gradient descent for high-speed flight

International Journal of Micro Air Vehicles ◽

10.1177/1756829319833685 ◽

2019 ◽

Vol 11 ◽

pp. 175682931983368

Author(s):

S Li ◽

C De Wagter ◽

CC de Visser ◽

QP Chu ◽

GCHE de Croon

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Gradient Descent ◽

High Speed ◽

Time Window ◽

Kinematic Model ◽

Micro Air Vehicles ◽

Gradient Descent Method ◽

End Point ◽

Air Vehicles

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper the use of standard visual odometry approaches and imply that the micro air vehicles will have to bridge considerable time intervals without position feedback. To this end, we propose an approach to trajectory estimation for drone racing that is computationally efficient and yet able to accurately estimate a micro air vehicle’s state (including biases) and parameters based on sparse, noisy observations of racing gates. The key concept of the approach is to optimize unknown and difficult-to-observe state variables so that the observations of the racing gates best fit with the known control inputs, estimated attitudes, and the quadrotor dynamics and aerodynamics during a time window. It is shown that a gradient-descent implementation of the proposed approach converges ∼4 times quicker to (approximately) correct bias values than a state-of-the-art 15-state extended Kalman filter. Moreover, it reaches a higher accuracy, as the predicted end-point of an open-loop turn is on average only ∼20 cm away from the actual end-point, while the extended Kalman filter and the gradient descent method with kinematic model only reach an accuracy of ∼50 cm. Although the approach is applied here to drone racing, it generalizes to other settings in which a micro air vehicle may only have sparse access to velocity and/or position measurements.

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A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Sensors ◽

10.3390/s21175725 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5725

Author(s):

Mingliang Zhou ◽

Wen Cheng ◽

Hongwei Huang ◽

Jiayao Chen

Keyword(s):

Laser Scanning ◽

Laser Intensity ◽

Integrated Approach ◽

Detection Methods ◽

Depth Information ◽

Railway Tunnel ◽

Depth Images ◽

Novel Approach ◽

Proposed Model ◽

Spalling Defect

The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

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A novel approach for Identification of pills based on the method of Depth from Focus

Industria Textila ◽

10.35530/it.069.06.1271 ◽

2019 ◽

Vol 69 (06) ◽

pp. 466-471

Author(s):

YU LING JIE ◽

WANG RONG WU ◽

ZHOU JIN FENG

Keyword(s):

Laser Scanning ◽

Optical Microscope ◽

Depth Image ◽

Depth Information ◽

Scanning System ◽

Depth From Focus ◽

Depth Images ◽

Novel Approach ◽

Fabric Analysis ◽

Low Efficiency

For automatic pilling evaluation of textiles, the depth information is one of the most critical and effective features in extracting pills from fabric image. Laser-scanning techniques are often used for acquiring 3D depth images. However, due to the high-cost and low-efficiency of Laser-scanning system, researchers have found it unsuitable for fabric analysis. This paper illustrates a new approach for acquiring the depth image used to extract pills by introducing the method of Depth From Focus (DFF). This approach firstly captures a sequence of images of the same view at different focal positions under the automatic optical microscope. Then the best-focused position (z) of each pixel(x, y) was determined by choosing the layer of image declaring the max sharpness and formed the depth image. This paper proposed a new sharpness-evaluation criterion which was based on the variance of gradients. Afterwards, a few basic points indicating the background area was selected from the depth image, and then the depth coordinates (x, y, z) at these basic points were used to calculate a predicted background plane. Via the background plane, pills above the background were extracted. A fabric sample with a single fiber upon it was presented to illustrate the process and result of the approach.

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