Hear-and-avoid for unmanned air vehicles using convolutional neural networks

Wijnker, Dirk; Dijk, Tom van; Snellen, Mirjam; Croon, Guido de; Wagter, Christophe De

doi:10.1177/1756829321992137

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Very early on, the capabilities of image processing arose to help the pilot in flying and maintaining safety. Image processing proposals have been published for landing area recognition [5], as support to an emergency landing operation [6], and as a detect-and-avoid on-board system [7].…”

Section: Previous Workmentioning

confidence: 99%

Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

Royo

Asenjo

Trujillo

et al. 2022

Drones

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Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 metres. With our new methodology, these two drawbacks can be overcome and results with good accuracy (96% correct segmentation and between 20% and 35% mean average proportional error) can be obtained in less than 20 s.

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Section: Previous Workmentioning

confidence: 99%

Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

Royo

Asenjo

Trujillo

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

Dolph¹,

Ippolito²,

Glaab³

et al. 2023

Journal of Aerospace Information Systems

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This work proposes a vision-based perception algorithm that combines image-processing-based detection and tracking of aerial objects with convolutional neural networks (CNNs) integrated for classification of general aviation aircraft, multirotor small uncrewed aerial systems (SUAS), fixed-wing SUAS, and birds to enable improved onboard avoidance algorithm decision making. Furthermore, we integrate adversarial learning during the training of the CNNs and evaluate performance with class balanced and imbalanced datasets because this maximizes the utility of resource-expensive flight experiments to collect aviation datasets. We compare our proposed CNN with adversarial learning (CNN+ADVL) model with a state-of-the-art CNN as well as a you only look once (YOLO, v4) model retrained (YOLO v4 aircraft) on the same data. The CNN+ADVL trained on the imbalanced dataset achieves the highest 10-fold cross-validation classification accuracy of 76.2% for aircraft and birds for all ranges while achieving 87.0% aircraft classification accuracy, meeting proposed self-assurance separation distances derived from Federal Aviation Administration (FAA) guidelines. In comparison, the CNNs achieved 74.4% 10-fold cross-validation classification accuracy for aircraft and birds as well as 83.4% accuracy for the aircraft, meeting proposed self-assurance separation distances derived from FAA guidelines. Furthermore, we demonstrate that the integration of adversarial learning improves the classification performance for the perception of aerial objects using a class imbalanced dataset.

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Hear-and-avoid for unmanned air vehicles using convolutional neural networks

Cited by 2 publications

References 11 publications

Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

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