Deep learning for unmanned aerial vehicles detection: A review

Al-lQubaydhi, Nader; Alenezi, Abdulrahman; Alanazi, Turki; Senyor, Abdulrahman; Alanezi, Naif; Alotaibi, Bandar; Alotaibi, Munif; Razaque, Abdul; Hariri, Salim

doi:10.1016/j.cosrev.2023.100614

Cited by 31 publications

(3 citation statements)

References 91 publications

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“…В 2024 году вышла большая обзорная статья по их обнаружению [2], как по данным с радара, так и по звуку и видео (обнаружению в видео в статье уделено мало внимания). Для распознавания БПЛА на видео было предпринято множество различных решений: использовались классические алгоритмы, например, SVM [3], нейронные сети, и комбинированный подход [4].…”

Section: существующие решенияunclassified

UAV detection using neural networks

Averina,

Levanova,

Grushevskaya

et al. 2024

Model. anal. inf. sist.

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The availability of unmanned aerial vehicles (UAVs) has led to a significant increase in the number of offenses involving their use. This makes the development of UAV detection systems relevant. Solutions based on deep neural networks show the best results in detecting UAVs on video. This article presents a study of various neural network detectors and focuses on identifying objects as small as possible, up to the size of $4\times4$ and even $3\times3$ pixels. The work investigates architectures SSD (VGG16) and YOLOv3 and it's modifications. Precision and recall metrics are calculated separately for different intervals of the object areas. The best result have been shown by YOLOv3 model with bbox parameters chosen as the result of object sizes clustering. Small ($3\times3$ px) drones have been successfully identified with 76% precision and a very small recall of 26%. For objects between 10 and 20 pixels in area, the recall is 64% with an accuracy of 75%. For objects with an area more than 20px the recall is about 90%, the precision is 89%, and the F1 score is 90%. These results show that it is possible to recognize even $4\times4$ pixel drones, which can be used in video surveillance systems.

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Section: существующие решенияunclassified

UAV detection using neural networks

Averina,

Levanova,

Grushevskaya

et al. 2024

Model. anal. inf. sist.

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“…Traditional methods of detecting tassels often involve manual visual inspection or basic mechanical devices, which are inefficient and influenced by environmental and weather conditions, leading to low detection accuracy. However, recent advancements in deep learning (Al-lQubaydhi et al, 2024) [2] and unmanned aerial vehicles have enabled researchers to develop automated toolsthat enhance the precision and efficiency of these tasks (Guan et al, 2024) [3], thereby making tassel detection more accurate and efficient.…”

Section: Introductionmentioning

confidence: 99%

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

Niu,

Nie,

et al. 2024

Drones

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In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology.

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“…Since the computational complexity of traditional optimization-based algorithms mostly grows exponentially with the system size, heuristic algorithms such as the ACO algorithm and genetic algorithm (GA) become better choices that can obtain a near-optimal solution in a shorter time. However, they are not suitable for real-time planning in rapidly changing environments, making online trajectory optimization considered to be implemented using machine learning algorithms [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network

Shen,

Wang,

Huang

et al. 2024

Sensors

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With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV’s flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

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Deep learning for unmanned aerial vehicles detection: A review

Cited by 31 publications

References 91 publications

UAV detection using neural networks

UAV detection using neural networks

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

Optimization of Clustering and Trajectory for Minimizing Age of Information in Unmanned Aerial Vehicle-Assisted Mobile Edge Computing Network

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