YOLO-Based UAV Technology: A Review of the Research and Its Applications

Chen, Chunling; Zheng, Ziyue; Xu, Tao; Guo, Shuang; Feng, Shuai; Yao, Weixiang; Lan, Yubin

doi:10.3390/drones7030190

Cited by 43 publications

(32 citation statements)

References 140 publications

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“…To test this we use the well known Yolo object detection model which has in recent years been successfully used in many remote sensing detection tasks. 11,14,15,44 For the purpose of this work the Yolo-v7 4 is used.…”

Section: Methodsmentioning

confidence: 99%

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Hatic,

Rauhut,

Polushko

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Natural disasters have devastating effects on communities, necessitating swift and accurate damage assessment. Manual assessment methods are time-consuming and costly, emphasizing the significance of semiautomatic approaches employing remote sensing and drone data. However, current datasets primarily focus on Western countries’ infrastructure, lacking information on damaged buildings in other regions specifically Africa. To bridge this gap, we present the EDDA dataset, comprising orthorectified mosaic images of rural and urban areas in Mozambique affected by Cyclone Idai. In this study, we utilize the EDDA dataset to evaluate the applicability of the lightweight object detection model YOLOv7 for efficient and timely disaster response. Testing the dataset with YOLOv7, assessed the datasets suitability for the task of building damage object detection under different class compositions and training data preprocessing configurations. Results showed promising results when utilizing Yolov7 as a building detector regardless of damage class, as a region proposal network, while building damage recognition requires additional research.

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Section: Methodsmentioning

confidence: 99%

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Hatic,

Rauhut,

Polushko

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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“…Over the past decade, artificial intelligence has led to significant progress in the domain of computer vision, automating image and video analysis tasks. Among computer vision methods, Convolutional Neural Networks (CNNs) are particularly promising for future advances in automating wildlife monitoring [6,[12][13][14][15][16][17][18]. Corcoran et al [3] concluded that when implementing automatic detection, fixed-winged drones with RGB sensors were ideal for detecting larger animals in open terrain, whereas, for small, elusive animals in more complex habitats, multi-rotor systems with infrared (IR) or thermal infrared sensors are the better choice, especially when monitoring cryptic and nocturnal animals.…”

Section: Automatic Detection and Computer Visionmentioning

confidence: 99%

“…A popular and open-source group of CNNs is the YOLO (You Only Look Once) object detection and image segmentation models, with several iterations and active development [14,[19][20][21][22], and a technology cross-fusion with drones has already been proposed as YOLO-Based UAV Technology (YBUT) [6]. The advantages of the YOLO models are that they are fast [8], making it possible to perform object detection in real-time on live footage, and that they are relatively user-friendly and intuitive, making the models approachable to non-computer scientists.…”

Section: You-only-look-once-based Uav Technologymentioning

confidence: 99%

“…The use of aerial drones for wildlife monitoring has increased exponentially in the past decade [1][2][3][4][5][6][7]. These drones, also known as uncrewed aerial vehicles (UAVs), unmanned aerial systems (UASs), and remotely piloted aircraft systems (RPASs), can carry a variety of sensors, including high-resolution visible-light-cameras (RGB) and thermal infrared (TI) cameras.…”

Section: Introductionmentioning

confidence: 99%

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Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Povlsen,

Bruhn,

Durdevic

et al. 2023

Drones

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Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

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“…In contrast, single-stage detection methods, unlike their two-stage counterparts, directly predict the category of the objects and position on the feature map using preset anchor points, accomplishing object detection in a single step and thereby enhancing computational speed. Notable instances of this methodology include SSD [16], RetinaNet [17], and YOLO [18][19][20]. Single-stage methods, when compared to two-stage ones, excel in real-time performance but usually lag slightly in accuracy.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection in UAV Images via Background Suppression Pyramid Network and Multi-Scale Task Adaptive Decoupled Head

Pan,

Xia,

et al. 2023

Remote Sensing

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Vehicle detection based on unmanned aerial vehicle (UAV) aerial images plays a significant role in areas such as traffic monitoring and management, disaster relief, and more, garnering extensive attention from researchers in recent years. However, datasets acquired from UAV platforms inevitably suffer from issues such as imbalanced class distribution, severe background interference, numerous small objects, and significant target scale variance, presenting substantial challenges to practical vehicle detection applications based on this platform. Addressing these challenges, this paper proposes an object detection model grounded in a background suppression pyramid network and multi-scale task adaptive decoupled head. Firstly, the model implements a long-tail feature resampling algorithm (LFRA) to solve the problem of imbalanced class distribution in the dataset. Next, a background suppression pyramid network (BSPN) is integrated into the Neck segment of the model. This network not only reduces the interference of redundant background information but also skillfully extracts features of small target vehicles, enhancing the ability of the model to detect small objects. Lastly, a multi-scale task adaptive decoupled head (MTAD) with varied receptive fields is introduced, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. Experimental results indicate that the proposed model achieves state-of-the-art performance on lightweight object detection networks. Compared to the baseline model PP-YOLOE-s, our model improves the AP50:95 on the VisDrone-Vehicle dataset by 1.9%.

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YOLO-Based UAV Technology: A Review of the Research and Its Applications

Cited by 43 publications

References 140 publications

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Vehicle Detection in UAV Images via Background Suppression Pyramid Network and Multi-Scale Task Adaptive Decoupled Head

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