Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging

Xie, Xiuchuan; Yang, Tao; Zhang, Yanning; Liang, Bang; Liu, Linfeng

doi:10.1049/cvi2.12053

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…In recent years, deep learning techniques, especially convolutional neural networks (CNN) [13][14][15], are rapidly becoming the preferred method to overcome the above-mentioned challenges [16][17][18][19][20]. Due to the scale invariance of the convolutional neural network, the image problem it solves is not limited by the scale and shows outstanding ability in recognition and classification.…”

Section: Deep Neural Network Methodsmentioning

confidence: 99%

Residual Self-Calibration and Self-Attention Aggregation Network for Crop Disease Recognition

Zhang

Sun

Cheng

et al. 2021

IJERPH

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The correct diagnosis and recognition of crop diseases play an important role in ensuring crop yields and preventing food safety. The existing methods for crop disease recognition mainly focus on accuracy while ignoring the algorithm’s robustness. In practice, the acquired images are often accompanied by various noises. These noises lead to a huge challenge for improving the robustness and accuracy of the recognition algorithm. In order to solve this problem, this paper proposes a residual self-calibration and self-attention aggregation network (RCAA-Net) for crop disease recognition in actual scenarios. The proposed RCAA-Net is composed of three main modules: (1) multi-scale residual module, (2) feedback self-calibration module, and (3) self-attention aggregation module. Specifically, the multi-scale residual module is designed to learn multi-scale features and provide both global and local information for the appearance of the disease to improve the performance of the model. The feedback self-calibration is proposed to improve the robustness of the model by suppressing the background noise in the original deep features. The self-attention aggregation module is introduced to further improve the robustness and accuracy of the model by capturing multi-scale information in different semantic spaces. The experimental results on the challenging 2018ai_challenger crop disease recognition dataset show that the proposed RCAA-Net achieves state-of-the-art performance on robustness and accuracy for crop disease recognition in actual scenarios.

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Section: Deep Neural Network Methodsmentioning

confidence: 99%

Residual Self-Calibration and Self-Attention Aggregation Network for Crop Disease Recognition

Zhang

Sun

Cheng

et al. 2021

IJERPH

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“…To improve object localisation, ref. [45] proposes relative motion estimation and global position optimisation methods. Ref.…”

Section: Related Workmentioning

confidence: 99%

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

Zarei,

Moallem,

Shams

2023

IET Computer Vision

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The position of vehicles is determined using an algorithm that includes two stages of detection and prediction. The more the number of frames in which the detection network is used, the more accurate the detector is, and the more the prediction network is used, the algorithm is faster. Therefore, the algorithm is very flexible to achieve the required accuracy and speed. YOLO's base detection network is designed to be robust against vehicle scale changes. Also, feature maps are produced in the detector network, which contribute greatly to increasing the accuracy of the detector. In these maps, using differential images and a u‐net‐based module, image segmentation has been done into two classes: vehicle and background. To increase the accuracy of the recursive predictive network, vehicle manoeuvres are classified. For this purpose, the spatial and temporal information of the vehicles are considered simultaneously. This classifier is much more effective than classifiers that consider spatial and temporal information separately. The Highway and UA‐DETRAC datasets demonstrate the performance of the proposed algorithm in urban traffic monitoring systems.

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CLaSP: Cross‐view 6‐DoF localisation assisted by synthetic panorama

Zhu,

Yan,

Cheng

et al. 2024

IET Computer Vision

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Despite the impressive progress in visual localisation, 6‐DoF cross‐view localisation is still a challenging task in the computer vision community due to the huge appearance changes. To address this issue, the authors propose the CLaSP, a coarse‐to‐fine framework, which leverages a synthetic panorama to facilitate cross‐view 6‐DoF localisation in a large‐scale scene. The authors first leverage a segmentation map to correct the prior pose, followed by a synthetic panorama on the ground to enable coarse pose estimation combined with a template matching method. The authors finally formulate the refine localisation process as feature matching and pose refinement to obtain the final result. The authors evaluate the performance of the CLaSP and several state‐of‐the‐art baselines on the Airloc dataset, which demonstrates the effectiveness of our proposed framework.

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Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging

Cited by 3 publications

References 38 publications

Residual Self-Calibration and Self-Attention Aggregation Network for Crop Disease Recognition

Residual Self-Calibration and Self-Attention Aggregation Network for Crop Disease Recognition

Real‐time vehicle detection using segmentation‐based detection network and trajectory prediction

CLaSP: Cross‐view 6‐DoF localisation assisted by synthetic panorama

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