Vehicle Detection From UAV Imagery With Deep Learning: A Review

Bouguettaya, Abdelmalek; Zarzour, Hafed; Kechida, Ahmed; Taberkit, Amine Mohammed

doi:10.1109/tnnls.2021.3080276

Cited by 91 publications

(40 citation statements)

References 150 publications

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“…Deep learning (DL) procedures like Convolutional Neural Networks (CNNs) were broadly recognized as a notable approach for numerous computer vision applications (classification, image or video recognition, and detection), and have revealed amazing outcomes in various applications [10]. Therefore, there comes numerous advantages to stopping from utilizing DL methods in emergency response and calamity management applications to restore crucial data in a timely manner and permitting superior research and response in the course of time-critical circumstances, and supporting the decisionmaking processes [11]. Although CNNs were rising successfully at several classification roles via transfer learning (TL), their interpretation speed on implanted platforms, like those discovered on-board UAVs, is hampered by the high computational cost, which may acquire and the model size of these networks is prohibitive from a memory standpoint for these entrenched gadgets [12].…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Alotaibi

Mengash²,

Negm³

et al. 2022

Applied Sciences

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Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.

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Section: Introductionmentioning

confidence: 99%

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Alotaibi

Mengash²,

Negm³

et al. 2022

Applied Sciences

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“…According to the different ideas of solving the mapping, there are mainly: influence diagram [2][3][4], genetic algorithm [5,6], fuzzy logic [7,8], neural network [9] and other methods. In response to different scenarios, these methods have been used to solve the problem of autonomous decision-making, and many results have been achieved [10][11][12]. However, these methods are still plagued by problems such as "dimensionality disaster", "human subjective influence", and "rule loopholes".…”

Section: Introductionmentioning

confidence: 99%

Operational Rule Extraction and Construction Based on Task Scenario Analysis

Zhao¹,

Wang²,

Cui³

et al. 2022

Information

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Changes in the information age have induced the necessity for a more efficient and effective self-decision-making requirement. A method of extracting and constructing naval operations decision-making rules based on scenario analysis is proposed. The template specifications of Event Condition Action (ECA) rules are defined, and a consistency detection method of ECA rules based on SWRL is proposed. The logical relationships and state transitions of the naval operational process is analyzed in detail, and the association of objects, events, and behaviors is realized. Finally, the operation of the proposed methods is illustrated through an example process, showing the method can effectively solve the problems of self-decision-making rule extraction and construction among naval battlefield decision environment, and avoid relying on artificial intelligence, which may have brought some uncertain factors.

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“…Recently, the images and videos of UAV have been widely used in vast areas, such as high-altitude remote sensing image acquisition [1], geographic information collection [2], surveying and mapping system development [3], agricultural applications [4] etc. The growing interest in UAVs has made UAV MOD an hot topic, and more and more important for UAV applications [5].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the above-mentioned efforts, there is still a room for improvement on the MOD because of serious interference, such as various types of noise and low resolution images, which hamper the detection accuracy [5] . Besides, different sizes and numbers of the moving objects, also introduced different impacts on the algorithm analysis and establishment of the model [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Moving Object Detection in YOLO UAV Videos

Hu¹,

Hu²,

Wang³

et al. 2022

Journal of Compurters

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<p>Recently, moving object detection (MOD) in UAV (Unmanned Aerial. Vehicle) videos has been widely used in many fields. However, different objects and different algorithms often result in different detection accuracy. SSD (Single Shot MultiBox Detector) series and YOLO (You Only Look Once) version 5 are two popular object detection model, and their performance are always evaluated and compared with other improved method for optimizing detection accuracy. In this paper, an improved YOLO_v5 detection algorithm was proposed to further improve the detection accuracy. It adopted a cascaded inter-frame verification mechanism which is based on the neural network and uses spatial information and integrates object speed and direction as well to improve the detection accuracy of moving objects. To evaluate its performance, the open UAV video data from Stanford University was used to test the algorithms, and three types of moving objects were analyzed. The experimental results demonstrate that the proposed MOD method can improve the detection accuracy of small moving objects, which have a good application value, and can lay a foundation for subsequent related studies.</p> <p> </p>

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Vehicle Detection From UAV Imagery With Deep Learning: A Review

Cited by 91 publications

References 150 publications

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Operational Rule Extraction and Construction Based on Task Scenario Analysis

An Improved Algorithm for Moving Object Detection in YOLO UAV Videos

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