Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

Moon, SungTae; Jeon, Jihun; Kim, Doyoon; Kim, Yongwoo

doi:10.1109/access.2022.3233841

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Building upon this hardware foundation, software development for the intrusion detection algorithm is undertaken. The device represents embedded AI processing Drones 2024, 8, 68 6 of 18 platforms, delivering robust, low-power computational support for tasks involving deep learning and computer vision [17][18][19][20]. The performance and specifications of the embedded AI onboard computer are outlined in Table 2.…”

Section: Hardware Designmentioning

confidence: 99%

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Xu,

Liu,

et al. 2024

Drones

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Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.

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Section: Hardware Designmentioning

confidence: 99%

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Xu,

Liu,

et al. 2024

Drones

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“…Modern unmanned aerial vehicles (UAVs) are widely used in the world for many applications, from delivering packages to performing combat missions on a battlefield. The most common civil task for UAVs is reconnaissance and searching for missing people over a large area [1]. Reconnaissance with the application of UAVs often uses Full Motion Video to embed spatial information in a video file and ensure the compatibility with standard geographic information system environments [2].…”

Section: Introductionmentioning

confidence: 99%

“…The most used UAV object detection method is YOLO. For example, Sungtae Moon applied the YOLOv5 model, adding the filter pruning method, and as a result, he achieved 40 % parameter reduction as compared to the base model [1].…”

Section: Introductionmentioning

confidence: 99%

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Tulembayeva,

Ashirov,

Makarov

et al. 2024

Acta Polytech

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In the last decade, unmanned aerial vehicles have been widely used in various civil and military field operations. Most modern drones are manually controlled by an operator and require decision making, which requires experience gained through training, which is time consuming and expensive, and even with experienced operators, the human factor can never be excluded. In this paper, an algorithm for an automatic flight mode of unmanned aerial vehicles without an operator’s participation has been developed. The proposed algorithm has been considered in the military reconnaissance of railways using edge computing and computer vision approaches to recognise railroads and moving trains for simulated and real cases as well as send report data to the web platform. As a result, the head of the operation received information about the number of carriages and Global Positioning System coordinates of the recognised train to make the necessary decisions.

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“…On the other hand, the application of multiple unmanned aerial swarms for autonomous cooperation, such as communication relay [37], reconnaissance [38], and emergency rescue [39] is currently receiving widespread attention. To reduce communication bandwidth and energy consumption, only several individuals are usually selected to receive task target information and guide the group to reach the target area.…”

Section: Introductionmentioning

confidence: 99%

ATI: Assemble topological interaction overcomes consistency–cohesion trade‐off in bird flocking

Huang,

Zhu,

2024

IET Cyber-Syst and Robotics

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In nature, various animal groups like bird flocks display proficient collective navigation achieved by maintaining high consistency and cohesion simultaneously. Both metric and topological interactions have been explored to ensure high consistency among groups. The topological interactions found in bird flocks are more cohesive than metric interactions against external perturbations, especially the spatially balanced topological interaction (SBTI). However, it is revealed that in complex environments, pursuing cohesion via existing interactions compromises consistency. The authors introduce an innovative solution, assemble topological interaction, to address this challenge. Contrasting with static interaction rules, the new interaction empowers individuals with self‐awareness to adapt to the complex environment by switching between interactions through visual cues. Most individuals employ high‐consistency k‐nearest topological interaction when not facing splitting threats. In the presence of such threats, some switch to the high‐cohesion SBTI to avert splitting. The assemble topological interaction thus transcends the limit of the trade‐off between consistency and cohesion. In addition, by comparing groups with varying degrees of these two features, the authors demonstrate that group effects are vital for efficient navigation led by a minority of informed agents. Finally, the real‐world drone‐swarm experiments validate the applicability of the proposed interaction to artificial robotic collectives.

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Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

Cited by 6 publications

References 35 publications

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

ATI: Assemble topological interaction overcomes consistency–cohesion trade‐off in bird flocking

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