Akos Zarandy

Bio: Akos Zarandy is an academic researcher from Hungarian Academy of Sciences. The author has contributed to research in topics: Vanishing point & Acceleration. The author has an hindex of 2, co-authored 10 publications receiving 20 citations. Previous affiliations of Akos Zarandy include Széchenyi István University & Pázmány Péter Catholic University.

Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid

Abstract: The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach, the horizontal relative distance and its direction and the vertical relative distance also. The distances are relative to the intruder aircraft horizontal and vertical sizes. The overall estimated relative distance is the closest between the two aircraft in three dimension. So finally, every important information can be extracted to be used in a collision decision. The applicability of the developed method is presented in software-in-the-loop simulation test runs. Several intruder size and speed values are considered together with trajectories covering the whole three dimensional space. The horizontal intruder flight directions relative to the own aircraft cover 360∘ and the intruder can come from below ar above also. Detailed evaluation and discussion of the results is also included. Finally, the missed detection rate results to be superior (below 3% in every test scenario) though the false alarm rate results a bit high between 7–14%.

Onboard Visual Horizon Detection for Unmanned Aerial Systems with Programmable Logic

Abstract: We introduce and analyze a fast horizon detection algorithm with native radial distortion handling and its implementation on a low power field programmable gate array (FPGA) development board in this paper. The algorithm is suited for visual applications in an airborne environment, that is on board a small unmanned aircraft. The algorithm was designed to have low complexity because of the power consumption requirements. To keep the computational cost low, an initial guess for the horizon is used, which is provided by the attitude heading reference system of the aircraft. The camera model takes radial distortions into account, which is necessary for a wide-angle lens used in most applications. This paper presents formulae for distorted horizon lines and a gradient sampling-based resolution-independent single shot algorithm for finding a horizon with radial distortion without undistortion of the complete image. The implemented algorithm is part of our visual sense-and-avoid system, where it is used for the sky-ground separation, and the performance of the algorithm is tested on real flight data. The FPGA implementation of the horizon detection method makes it possible to add this efficient module to any FPGA-based vision system.

Comparison of Mono Camera-based Static Obstacle Position Estimation Methods for Automotive Application

Abstract: This paper presents the comparison of four different mono camera-based steady obstacle position and size estimation algorithms focusing on automatic emergency braking application. Three methods are well known in the automotive field, the fourth is the author's own method successfully applied in aerospace until now. The first contribution is the extension of all methods to consider multiple data points and variable velocity (where possible). The second contribution is an extensive simulation testing of the methods considering constant and variable speeds, attitude uncertainties and the braking characteristic of real vehicles. The methods are evaluated basedon the worst case hitting speed of the obstacle and the precision of obstacle side distance and size estimation. The maximum speeds of applicability are determined for all methods and the results are commented in detail.

CNN Technology-Tools and Applications

Abstract: Cellular neural/nonlinear networks were invented almost 30 years ago. The CNN research led to high performance computational architectures, neuromorphic modells of the visual and auditory pathways, stability results for arrays of coupled dynamic processors, and for practical image processing results. The main stages of the research and the results are summarized in this paper.

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.

Abstract: This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations

Abstract: In recent years, Unmanned Aerial Vehicles (UAVs) are widely utilized in precision agriculture, such as tree plantations. Due to limited intelligence, these UAVs can only operate at high altitudes, leading to the use of expensive and heavy sensors for obtaining important health information of the plants. To fly at low altitudes, these UAVs must possess the capability of obstacle avoidance to prevent crashes. However, most current obstacle avoidance systems with active sensors are not applicable to small aerial vehicles due to the cost, weight, and power consumption constraints. To this end, this paper presents a novel approach to the autonomous navigation of a small UAV in tree plantations only using a single camera. As the monocular vision does not provide depth information, a machine learning model, Faster Region-based Convolutional Neural Network (Faster R-CNN), was trained for the tree trunk detection. A control strategy was implemented to avoid the collision with trees. The detection model uses image heights of detected trees to indicate their distances from the UAV and image widths between trees to find the widest obstacle-free space. The control strategy allows the UAV to navigate until any approaching obstacle is detected and to turn to the safest area before continuing its flight. This paper demonstrates the feasibility and performance of the proposed algorithms by carrying out 11 flight tests in real tree plantation environments at two different locations, one of which is a new place. All the successful results indicate that the proposed method is accurate and robust for autonomous navigation in tree plantations.

Detection of a moving UAV based on deep learning-based distance estimation

Abstract: Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. A quadrotor is regarded as an owned UAV, and it is able to estimate the distance of an incoming fixed-wing intruder. The adopted object detection method is based on the you only look once (YOLO) object detector. Deep neural network (DNN) and convolutional neural network (CNN) methods are applied to exam their performance in the distance estimation of moving objects. The feature extraction of fixed-wing UAVs is based on the VGG-16 model, and then its result is applied to the distance network to estimate the object distance. The proposed model is trained by using synthetic images from animation software and validated by using both synthetic and real flight videos. The results show that the proposed active vision-based scheme is able to detect and track a moving UAV with high detection accuracy and low distance errors.

Sense and avoid technology developments at Queensland University of Technology

Abstract: Unmanned Aircraft, which are often referred as Remote Piloted Aircraft (RPA), Unmanned Aerial Vehicle (UAV) or Unmanned Aerial System (UAS), are a crucial technology in the future growth of industry. The seamless integration will depend on many technological advancements, and Sense and Avoid is considered the pinnacle of these. This paper presents an overview of the most significant developments in Sense and Avoid conducted at Queensland University of Technology, Australian Research Centre for Aerospace Automation in recent years. Our motivation has been the development of an electro-optical approach suitable for small to medium unmanned aircraft having challenging size, power and weight constraints. As such, we have created a system consisting of two main functional elements including a detection module and a decision and control module. A number of functionalities within each module are demonstrated in simulations and real-world flight test using fixed-wing as well as rotary wing platforms. We provide a brief description of the outcomes of each of the development phases of this program.