The Coercive Logic of Militant Drone Use

Doctor, Austin C.; Walsh, James Igoe

doi:10.55540/0031-1723.3069

Cited by 2 publications

(4 citation statements)

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“…Both studies failed to find evidence to support this. Rather, non-state actors use armed UAVs tactically against armed forces [10,11] and for theatrical and media purposes [11,13]. However, some important exceptions to these patterns are well known, such as the Houthi attacks on Saudi airports [46].…”

Section: How Armed Uavs Are Used By Non-state Actorsmentioning

confidence: 99%

“…Past research has focused, among other things, on the technological development of UAVs by violent non-state actors [8,9], tactical use and human harm [10][11][12], UAVs as a means to communicate and present violent non-state actors as sovereign legitimate actors [13], and on the proliferation of UAVs [14]. However, none of these covered or aimed to cover, more broadly, the field of violent non-state actors and armed UAVs.…”

Section: Introductionmentioning

confidence: 99%

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A Flying Reign of Terror? The Who, Where, When, What, and How of Non-state Actors and Armed Drones

Haugstvedt

2023

Journal of Human Security

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Over the last five years, violent non-state actors have acquired armed unmanned aerial vehicles (UAVs) and have been using them extensively. This paper presents the main non-state actors involved and the areas in which they have used this tool, as well as how UAVs are used and procured. To date, armed UAVs have mainly been used by non-state actors in the Middle East and Central Asia. They have also been used in the conflict zones of Ukraine, Myanmar, Mexico, and Ecuador. While this is worrisome, limited evidence suggests that violent non-state actors use armed UAVs intentionally in areas where mostly civilians are present. The paper details the state of UAV usage by non-state actors and develops a thesis of cyclic adaptation between state and non-state actors. Not only do non-state actors learn from state actors, so does state and state-backed actors learn from non-state actors in conflict zones.This process have been visible on the battlefield in Ukraine, where state-backed actors on both sides have incorporated smaller consumer style UAVs into their repertoire. As the use of armed UAVs developed substantially following Hezbollah's early UAV operations in 2004 and spread to many regions of the world, the adaptation of non-state cleverness and ingenuity can be harnessed by state actors in times of poor or limited access to weaponry and support systems.

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Section: How Armed Uavs Are Used By Non-state Actorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Flying Reign of Terror? The Who, Where, When, What, and How of Non-state Actors and Armed Drones

Haugstvedt

2023

Journal of Human Security

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“…In KNNbased object detection algorithm, predicted bounding boxes from the model are generated. Intersection of Union is determined using (1). IoU evaluation metric is selected because machine learning based object detection has varying parameters beyond the classification algorithm itself.…”

Section: Giou/iou Bounding Box Regressionmentioning

confidence: 99%

“…Nowadays, unmanned aerial vehicle (UAV), commonly known as drone technology, gets special attention not only in industries but also among researchers. To date, drone technology has been mainly used prior to military purposes [1]. But recently, drones have been widely used for private purposes such as monitoring agricultural crops and mining [2], recognizing traffic flow [3], investigation of disaster damage [4], and delivering goods to difficult areas [5], even for cinematic purposes [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone

2022

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In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.

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The Coercive Logic of Militant Drone Use

Cited by 2 publications

References 0 publications

A Flying Reign of Terror? The Who, Where, When, What, and How of Non-state Actors and Armed Drones

A Flying Reign of Terror? The Who, Where, When, What, and How of Non-state Actors and Armed Drones

Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone

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