Computer Vision Based Drone Detection Using Mask R-CNN

Mubarak, Auwalu Saleh; Vubangsi, M.; Al‐Turjman, Fadi; Ameen, Zubaida Said; Mahfudh, Abdulmunaim Saleh; Alturjman, Sinem

doi:10.1109/aie57029.2022.00108

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…However, Faster-RCNN with the VGG-16 version was a superior fusion option for the majority of trackers. Drone detection using a two-stage Mask R-CNN detector with two backbones, such as residual network (ResNet)-50 and MobileNet, was presented in [ 70 ]. The model was trained on a dataset [ 71 ] with 1000 images of drones.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…The main steps of the image acquisition process are: energy is reflected from the UAV, the reflected energy is focused by an optical system, and a camera sensor measures the amount of energy.Related works on drone object detection based on visual data can be divided into two major categories depending on whether the authors employed a deep learning model or dealt with feature extraction using traditional handcrafted features. However, recent studies on visual-based drone detection and identification have mostly employed learned features or deep learning models[61][62][63][64][65][66][67][68][69][70][71]. Deep learning for object detection with convolutional neural networks (CNNs) has two approaches: one-stage detection and two-stage detection.…”

mentioning

confidence: 99%

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Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

mentioning

confidence: 99%

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Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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show abstract

“…On the one hand, a region proposal network (RPN) [39] was used to determine the foreground and background for binary classification and generate candidate boxes. On the other hand, the generated candidate box corresponded to a pixel in the feature map, which was matched using the RoI Align [40] operation [41]. After that, one branch was used for classification and regression, while the other was used for segmentation to generate masks.…”

Section: Semantic Segmentationmentioning

confidence: 99%

A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments

Chen

Shang

et al. 2022

Micromachines

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A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or geometric methods cannot filter out dynamic feature points well. Considering the problem of dynamic objects easily interfering with the localization accuracy of SLAM systems, this paper proposes a new monocular SLAM algorithm for use in dynamic environments. This improved algorithm combines semantic information and geometric methods to filter out dynamic feature points. Firstly, an adjusted Mask R-CNN removes prior highly dynamic objects. The remaining feature-point pairs are matched via the optical-flow method and a fundamental matrix is calculated using those matched feature-point pairs. Then, the environment’s actual dynamic feature points are filtered out using the polar geometric constraint. The improved system can effectively filter out the feature points of dynamic targets. Finally, our experimental results on the TUM RGB-D and Bonn RGB-D Dynamic datasets showed that the proposed method could improve the pose estimation accuracy of a SLAM system in a dynamic environment, especially in the case of high indoor dynamics. The performance effect was better than that of the existing ORB-SLAM2. It also had a higher running speed than DynaSLAM, which is a similar dynamic visual SLAM algorithm.

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“…To address this issue and reduce accidents, computer vision can be integrated into drones. Computer vision is a cutting-edge field of study that focuses on enabling computers and machines to interpret and comprehend the visual world [4][5]. It involves developing algorithms and systems that can analyze images and videos to recognize objects, understand their meaning, and extract valuable information [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application

Jack Lee,

Hazry,

Muhammad Azizi

et al. 2023

J. Phys.: Conf. Ser.

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This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm’s accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm’s reliable performance in predicting distance measurements.

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Computer Vision Based Drone Detection Using Mask R-CNN

Cited by 5 publications

References 22 publications

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments

Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application

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