Monocular vehicle pose estimation based on 3D model

Xu, Lingzhi; Fu, Qinwei; TAO, Wei; Zhao, Hui

doi:10.37188/ope.20212906.1346

Cited by 4 publications

(1 citation statement)

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“…The pixels around key points account for the main body of loss in the later period of training. Still, the number of pixels around key points only accounts for a small part of all pixels of aircraft targets, making it more challenging to optimize the main part of the hole pixels [53]. In this paper, an alterable vector field loss function is proposed to enhance the pixel learning of the principal amount as follows:…”

Section: Vector Field Loss Function Optimizationmentioning

confidence: 99%

A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles

et al. 2021

Sensors

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This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity. In recent years, target detection models dependent on deep learning and pose estimation methods relying on a single RGB image have made significant progress. Based on these advanced algorithms, this paper proposes a method for measuring the actual angles of landing gears in two-dimensional images. A single RGB image of an aircraft is inputted to the target detection module to obtain the key points of landing gears. The vector field network votes the key points of the fuselage after extraction and scale normalization of the pixels inside the aircraft prediction box. Knowing the pixel position of the key points and the constraints on the aircraft, the angle between the landing gear and fuselage plane can be calculated even without depth information. The vector field loss function is improved based on the distance between pixels and key points, and synthetic datasets of aircraft with different angle landing gears are created to verify the validity of the proposed algorithm. The experimental results show that the mean error of the proposed algorithm for the landing gears is less than 5 degrees on the light-varying dataset.

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Section: Vector Field Loss Function Optimizationmentioning

confidence: 99%