Car Detection in Aerial Images of Dense Urban Areas

Elmikaty, Mohamed; Stathaki, Tania

doi:10.1109/taes.2017.2732832

Cited by 15 publications

(8 citation statements)

References 38 publications

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“…We propose an outlier-aware re-scoring function defined in Eq. (10). In OA-NMS, we utilizeγ andξ to represent the average area and aspect ratio of bounding boxes in one image.…”

Section: Outlier-aware Nmsmentioning

confidence: 99%

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Pan

2020

IEEE Access

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Recently, unmanned aerial vehicles (UAV) are widely used in many fields due to the low cost and high flexibility. One of the most popular applications of UAV is vehicle detection in aerial images which plays an important role in traffic surveillance and urban planning. Although, many deep learning based detectors have achieved state-of-the-art (SOTA) performance in natural images, the significant variation in object scales caused by the altitude change of the UAV platform brings great challenges to these detectors for precise localization of vehicles in aerial images. To improve the detection performance for vehicles with different scales, we propose a novel detection algorithm which consists of three stages. In the first stage, to reduce the distortion of vehicles during image resizing and keep more information of aerial images, we utilize an image cropping strategy to divide the image into two patches. In the second stage, we combine the original image and two patches into a batch and detect vehicles with a Convolutional Neural Network (CNN). For feature representation in our detector, we propose Scale-specific Prediction to strengthen the multi-scale features of vehicles with context information. In the final stage, to fuse detections and suppress false alarms, we propose an Outlier-Aware Non-Maximum Suppression. Extensive experiments are conducted to demonstrate the superiority of the proposed algorithm by comparison with other SOTA solutions.

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“…We propose an outlier-aware re-scoring function defined in Eq. (10). In OA-NMS, we utilizeγ andξ to represent the average area and aspect ratio of bounding boxes in one image.…”

Section: Outlier-aware Nmsmentioning

confidence: 99%

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Pan

2020

IEEE Access

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“…Unmanned aerial vehicles (UAVs) have a lot of acceptance in the control theory research due to the challenge of getting a stable flight and finding some application to solve some science and engineering problems. Some applications with these unmanned aerial vehicles are: forest fire detection, in civil engineering (topography, analysis structural and others) [1], photogrammetry, and military applications [2], car detection [3] or for landing [4]. We can find different strategies to resolve the trajectory tracking with a quadrotor; for example, ref.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in [14], a robust controller to stabilize a quadrotor is proposed; the control law was based on the proportional-derivative control, and to obtain a better flight performance a disturbance compensation was introduced. The mathematical model that defines the UAV in [14] is rotation in SO (3). It is worth mentioning that in [11][12][13][14][15] do not applying any adaptive controller.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking with Adaptive Robust Control for Quadrotor

et al. 2021

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This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

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“…The development and use of unmanned aerial systems (UAVs) have been increasing in the last decade [1], [2], and the theory about adaptive control is fundamental in the development and advances in this field. And even the applications of the fixed-wing UAVs are increasing; some applications are: forest fire detection, in civil engineering (topography, analysis structural and others) [3], photogrammetry, and military applications [4], car detection [5] or for landing [6]. We can find some works in the scientific literature referents to adaptive control based on the MIT rule.…”

Section: Introductionmentioning

confidence: 99%

Adjustment mechanism with sliding mode for adaptive PD controller applied to unmanned fixed-wing MAV altitude

Espinoza-Fraire

Saenz-Esqueda

́ınez

2021

IJRA

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<p>This work presents an adjustment mechanism with the sliding modes technique to design a proportional derivative (PD) controller with adaptive gains. The objective and contribution are to design a robust adjustment mechanism in the presence of unknown and not modeled perturbations in the system; this perturbation can be considered wind gusts. The robust adjustment mechanism is designed with the MIT rule and the gradient method with the sliding mode theory. The adaptive PD obtained is applied to regulate unmanned fixed-wing miniature aerial vehicle (MAV’s) altitude.</p>

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Car Detection in Aerial Images of Dense Urban Areas

Cited by 15 publications

References 38 publications

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Trajectory Tracking with Adaptive Robust Control for Quadrotor

Adjustment mechanism with sliding mode for adaptive PD controller applied to unmanned fixed-wing MAV altitude

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