Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Vera-Yanez, Daniel; Pereira, António; Rodrigues, Nuno; Molina, José Pascual; García, Arturo S.; Fernández-Caballero, Antonio

doi:10.3390/jimaging9100194

Cited by 3 publications

(2 citation statements)

References 40 publications

(40 reference statements)

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“…This comparative simplicity, combined with advanced analytical capabilities, positions monocular cameras as a potent tool in the arsenal against mid-air collisions, balancing technical sophistication with operational pragmatism [16]. These reasons could elucidate why our systematic review [17] determined that monocular cameras are the favored option.…”

Section: Related Workmentioning

confidence: 98%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

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The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

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Section: Related Workmentioning

confidence: 98%

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vera-Yanez,

Pereira,

Rodrigues

et al. 2024

Sensors

Self Cite

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“…Most studies on the object detection of UAVs are focused on ground or static targets [26]. There are still many challenges in moving object detection, especially for mid-air and fixed-wing UAVs [28]. Compared to multirotor UAVs, fixed-wing UAVs have higher aerodynamic efficiency and performance, can carry more payload, and can travel longer distances.…”

Section: Vision-based Object Detection With Deep Learning Technologymentioning

confidence: 99%

Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

Lai,

Lin

2024

Remote Sensing

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With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of three phases: long-distance object detection, object region estimation, and collision risk assessment and collision avoidance. For long-distance object detection, an optical flow-based background subtraction method is utilized to detect an intruder far away from the host. A mask region-based convolutional neural network (Mask R-CNN) model is trained to estimate the region of the intruder in the image. Finally, the collision risk assessment adopts the area expansion rate and bearing angle of the intruder in the images to conduct mid-air collision avoidance based on visual flight rules (VFRs) and conflict areas. The proposed collision avoidance approach is verified by both simulations and experiments. The results show that the system can successfully detect different sizes of fixed-wing intruders, estimate their regions, and assess the risk of collision at least 10 s in advance before the expected collision would happen.

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Aircraft Innovation Trends Enabling Advanced Air Mobility

Bridgelall

2024

Inventions

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This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational effectiveness. This research utilizes a methodical approach that juxtaposes a systematic review of patents with an extensive analysis of the academic literature to map the innovation landscape of VTOL technology. This dual analysis reveals a dynamic progression in VTOL advancements, highlighting significant strides in aerodynamic optimization, propulsion technology, and control systems. The novelty of this study lies in its dual-method approach, combining patent analysis with the academic literature to provide a holistic view of VTOL technological evolution. The patent analysis reveals that companies have been most productive on innovations relating to VTOL aircraft transition efficiency, control enhancement, and energy management. The literature review identifies key trends such as the rise in electric propulsion technologies and the integration of AI-driven control mechanisms. These results provide new engineering knowledge that can guide future VTOL development and policy formulation. The original contributions include a detailed mapping of VTOL innovation trends, identification of key technological advancements, and a predictive lens into future directions. These findings offer a valuable resource for aerospace engineers, policymakers, and urban planners. This study contributes a detailed assessment of both theoretical foundations and practical applications, fostering a holistic view of the challenges and innovations shaping the future of AAM. By connecting research and practical development, this study serves as a critical tool for strategic decision making and policy formulation towards advancing the integration of VTOL aircraft into sustainable urban transportation networks.

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Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

Cited by 3 publications

References 40 publications

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance

Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

Aircraft Innovation Trends Enabling Advanced Air Mobility

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