A Novel Object Detection Method in City Aerial Image Based on Deformable Convolutional Networks

Zhou, Liqun; Sun, Peng; Li, De; Piao, Jin-Chun

doi:10.1109/access.2022.3156953

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…On the other hand, Unmanned Aerial Vehicles (UAVs), flycams, and drones are increasingly being used in various fields such as agriculture, commerce, security, as well as in daily activities like filming or aerial photography [1,2].…”

Section: Introductionmentioning

confidence: 99%

Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Vo,

Ngo,

et al. 2024

IEEE Access

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The development of technological devices, such as satellites and drones, has made it easier to collect images and videos from above. With rich and diverse data sources, the problem of detecting objects in aerial images is formed to serve real-life situations: rescue missions, social scenarios, security needs, and more. This issue attracts a lot of attention from researchers in computer vision because of its high applicability in real life. However, object detection in images collected from drones faces many significant challenges due to the complex factors of real-life environments. To overcome these difficulties, many studies focus on developing effective models for detecting and tracking objects from UAV systems. In this paper, our primary emphasis is comprehensive survey of methodologies pertaining to horizontal and oriented object detection. We analyzed and compared existing approaches such as CNN-based vs. Transformerbased, Anchor-based vs. Anchor-free. Moreover, we also learned about ways to represent objects using bounding boxes in oriented object detection. After researching many practical and innovative techniques in different approaches, we have carefully selected 15 methods for horizontal object detection and 18 for oriented object detection that were recently researched and developed to conduct classifications, surveys and assessments. We also provide a taxonomy mind map to assist readers in gaining an overview of the methods we surveyed. Additional, we examine 17 aerial image datasets collected from platforms and in various locations worldwide. These are typical datasets used to serve two primary tasks: Horizontal Object Detection and Oriented Object Detection. Then, we compared the experimental resultsof the previously introduced methods on four typical datasets: VisDrone, UAVDT, DOTAv1.0, and HRSC2016. The above datasets present diversity in the number of classes and the complexity of real-life conditions. Furthermore, they have been tested with various methods and shown remarkable results. For the reasons mentioned, we have enough basis to confirm that the comparison results are objective so that other research groups can make accurate comments and assessments on experimental methods on each dataset. We believe that updating and synthesizing the latest research will provide complete and comprehensive information, helping researchers to have more survey material for the development of the field of object detection in the future.INDEX TERMS aerial dataset, aerial image object detection, oriented object detection, and horizontal object detection.

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Section: Introductionmentioning

confidence: 99%

Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Vo,

Ngo,

et al. 2024

IEEE Access

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“…According to deformable convolutional networks (DCNs) (Zhou et al, 2022) and box boundaryaware vectors (BBAVs) (Yi et al, 2020), a new object detector oriented to aerial imagery, called DCN-BBAV, is proposed. It introduces adaptive point representations for different orientations, shapes, and attitudes.…”

Section: Introductionmentioning

confidence: 99%

Fusing DCN and BBAV for Remote Sensing Image Object Detection

Chen,

Wang

2024

International Journal of Cognitive Informatics and Natural Intelligence

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At the oriented object detection in aerial remote sensing images, the perceptual field boundaries of ordinary convolutional kernels are often not parallel to the boundaries of the objects to be detected, affecting the model precision. Therefore, an object detection model (DCN-BBAV) that fuses deformable convolution networks (DCNs) and box boundary-aware vectors (BBAVs) is proposed. Firstly, a BBAV is used as the baseline, replacing the normal convolution kernels in the backbone network with deformable convolution kernels. Then, the spatial attention module (SAM) and channel attention mechanism (CAM) are used to enhance the feature extraction ability for a DCN. Finally, the dot product of the included angles of four adjacent vectors are added to the loss function of the rotation frame parameter, improving the regression precision of the boundary vector. The DCN-BBAV model demonstrates notable performance with a 77.30% mean average precision (mAP) on the DOTA dataset. Additionally, it outperforms other advanced rotating frame object detection methods, achieving impressive results of 90.52% mAP on VOC07 and 96.67% mAP on VOC12 for HRSC2016.

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“…Urban Sci. 2023, 7, 108 2 of 14 Given these challenges, the research aims to address sustainable transport [12] objectives by utilizing unmanned aerial vehicles (UAVs) [13][14][15][16][17][18][19] in conjunction with Object Detection technology [20][21][22][23][24][25][26][27] to achieve real-time traffic monitoring. By leveraging the high maneuverability of UAVs and the precision of object detection, the goal is to effectively monitor traffic conditions and provide valuable traffic information to optimize traffic flow, improve road safety, and enhance the efficiency and accessibility of public transportation.…”

Section: Introductionmentioning

confidence: 99%

Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities

Chung,

Wang,

Lin

2023

Urban Science

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Environmental, social, and governance issues have gained significant prominence recently, particularly with a growing emphasis on environmental protection. In the realm of heightened environmental concerns, unmanned aerial vehicles have emerged as pivotal assets in addressing transportation challenges with a sustainable perspective. This study focuses on enhancing unmanned aerial vehicles’ object detection proficiency within the realm of sustainable transportation. The proposed method refines the YOLOv7 E-ELAN model, tailored explicitly for traffic scenarios. Leveraging strides in deep learning and computer vision, the adapted model demonstrates enhancements in mean average precision, outperforming the original on the VisDrone2019 dataset. This approach, encompassing model component enhancements and refined loss functions, establishes an efficacious strategy for precise unmanned aerial vehicles object detection. This endeavor aligns seamlessly with environmental, social, and governance principles. Moreover, it contributes to the 11th Sustainable Development Goal by fostering secure urban spaces. As unmanned aerial vehicles have become integral to public safety and surveillance, enhancing detection algorithms cultivates safer environments for residents. Sustainable transport encompasses curbing traffic congestion and optimizing transportation systems, where unmanned aerial vehicle-based detection plays a pivotal role in managing traffic flow, thereby supporting extended Sustainable Development Goal 11 objectives. The efficient utilization of unmanned aerial vehicles in public transit significantly aids in reducing carbon footprints, corresponding to the “Environmental Sustainability” facet of Environmental, Social, and Governance principles.

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A Novel Object Detection Method in City Aerial Image Based on Deformable Convolutional Networks

Cited by 9 publications

References 47 publications

Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Aerial Data Exploration: An in-Depth Study From Horizontal to Oriented Viewpoint

Fusing DCN and BBAV for Remote Sensing Image Object Detection

Advancing ESG and SDGs Goal 11: Enhanced YOLOv7-Based UAV Detection for Sustainable Transportation in Cities and Communities

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