SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Truong, Noi Quang; Lee, Young Won; Owais, Muhammad; Nguyen, Dat Tien; Batchuluun, Ganbayar; Pham, Tuyen Danh; Park, Kang Ryoung

doi:10.3390/s20143918

Cited by 21 publications

(14 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the default logic used in the AprilTag system for choosing the true pose from possible poses. As for the unsolved initial error problem, a deep learning-based image deblurring approach, such as DeblurGAN [22] is a promising solution for cases where the perspective-effect is not ideal.…”

Section: Resultsmentioning

confidence: 99%

Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Liu,

Schofield,

Shan

2021

Preprint

View full text Add to dashboard Cite

Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360 • vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and wheel odometry. Furthermore, an algorithm is proposed to resolve the rotational ambiguity problem using the prediction of the Kalman Filter as additional information. Finally, the lateral and longitudinal controllers are provided. Experiments are conducted to illustrate the effectiveness of the theory.

show abstract

Section: Resultsmentioning

confidence: 99%

Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Liu,

Schofield,

Shan

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Truong et al (2020) employed Skip Connection and Network (DCSCN) topology to create a super-resolution image-based posed estimation [5], while the authors of [6] applied LightDenseYOLO for tag tracking, leading to an accurate localization of the tag in the image. Another study concentrated on deep learning, utilizing SlimDeblurGAN architecture to de-blur images for landing tag detection, leading to a better-quality image when the image motion noise increased due to camera shaking or when gimbals are not available for stabilization [7]. A multi-layer perceptron and ultrasonic sensors mounted on the four arms of a quadrotor were used to estimate the landing surface suitability in [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Unstable Landing Platform Pose Estimation Based on Camera and Range Sensor Homogeneous Fusion (CRHF)

Sefidgar

Landry

2022

Drones

View full text Add to dashboard Cite

Much research has been accomplished in the area of drone landing and specifically pose estimation. While some of these works focus on sensor fusion using GPS, or GNSS, we propose a method that uses sensors, including four Time of Flight (ToF) range sensors and a monocular camera. However, when the descending platform is unstable, for example, on ships in the ocean, the uncertainty will grow, and the tracking will fail easily. We designed an algorithm that includes four ToF sensors for calibration and one for pose estimation. The landing process was divided into two main parts, the rendezvous and the final landing. Two important assumptions were made for these two phases. During the rendezvous, the landing platform movement can be ignored, while during the landing phase, the drone is assumed to be stable and waiting for the best time to land. The current research modifies the landing part as a stable drone and an unstable landing platform, which is a Stewart platform, with a mounted AprilTag. A novel algorithm for calibration was used based on color thresholding, a convex hull, and centroid extraction. Next, using the homogeneous coordinate equations of the sensors’ touching points, the focal length in the X and Y directions can be calculated. In addition, knowing the plane equation allows the Z coordinates of the landmark points to be projected. The homogeneous coordinate equation was then used to obtain the landmark’s X and Y Cartesian coordinates. Finally, 3D rigid body transformation is engaged to project the landing platform transformation in the camera frame. The test bench used Software-in-the-Loop (SIL) to confirm the practicality of the method. The results of this work are promising for unstable landing platform pose estimation and offer a significant improvement over the single-camera pose estimation AprilTag detection algorithms (ATDA).

show abstract

“…In the primary work, the generative adversarial network generates realistic-looking images from random noise input [ 16 ]. GAN exhibits an impressive performance in image super-resolution [ 17 , 18 ], image editing [ 19 , 20 ], image generation, style transfer [ 21 , 22 ], representation learning [ 23 , 24 ], object detection [ 9 , 10 , 25 ], and so on. GAN includes a generator and a discriminator: the generator generates images, and the discriminator determines the authenticity of images.…”

Section: Related Workmentioning

confidence: 99%

Centered Multi-Task Generative Adversarial Network for Small Object Detection

Wang

Bai

et al. 2021

Sensors

View full text Add to dashboard Cite

Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.

show abstract

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Cited by 21 publications

References 35 publications

Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Navigation of a Self-Driving Vehicle Using One Fiducial Marker

Unstable Landing Platform Pose Estimation Based on Camera and Range Sensor Homogeneous Fusion (CRHF)

Centered Multi-Task Generative Adversarial Network for Small Object Detection

Contact Info

Product

Resources

About