Prof. Guoli Wang Profile

Prof. Guoli Wang

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Proceedings Article | 10 August 2023 Paper

Adaptive intelligent particle filter for visual localization of mobile robot

Lin Li, Guoli Wang, Rihui Chen, Hongxi Ou, Chaoran Chen, Xuemei Guo

Proceedings Volume 12759, 127592F (2023) https://doi.org/10.1117/12.2686497

KEYWORDS: Particles, Motion models, Particle filters, Gold, Motion estimation, Mobile robots, Visualization, Particle swarm optimization, Performance modeling, Error analysis

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In the field of unmanned ships, which has attracted widly attention in recent years, the small terminals distributed on the huge ship need to independently judge the collision risks. However, the performance of terminal devices is significantly limited to ambient noise which affect the docking guidance. In this paper, Adaptive Intelligent Particle Filter (AIPF), an assisted tracking method based on Intelligent Particle Filter (IPF) is proposed to smooth and continuous state observation, thus effectively eliminating abnormal and leaping data. Specifically, this method regards the coordinates of the target in a period of time as a process with stationary independent increment, and the possible motion model is explored from an engineering perspective, so as to optimize low weight particles and adjust the number of particles dynamically. The experimental results on the ARMv7 platform demonstrate that even under the harsh conditions of computational power, the suggested method can still reduce particle degeneracy, improve the performance of particle swarm to better achieve auxiliary tracking.

Proceedings Article | 26 October 2022 Paper

Multi-scale feature reconstruction for unsupervised defect detection and localization

Younglun Xie, Guoli Wang, Xuemei Guo

Proceedings Volume 12452, 124520A (2022) https://doi.org/10.1117/12.2664584

KEYWORDS: Defect detection, Feature extraction, Data modeling, Image restoration, Reconstruction algorithms

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In recent years, the rapid development of deep learning makes it more and more widely used in the field of defect detection. Compared with the traditional machine vision methods, the deep learning methods based on Convolutional Neural Networks (CNN) have stronger feature learning abilities and can achieve higher detection accuracy and work efficiency in the field of surface defect detection of industrial products. However, supervised deep learning algorithms require a large amount of labeled data, making it difficult to generalize practically. To this end, we propose an unsupervised defect detection method MSFR-VAE for Multi-Scale Feature Reconstruction-Variational Auto Encoder: It realizes defect detection and localization by reconstructing the deep features of the input image and only needs to be trained on normal samples. Different from the image-based reconstruction, the feature-based reconstruction method can make the model focus more on the key features that can distinguish the normal and defective samples, so as to improve the detection effect. Besides, we use the pre-trained CNN for Multi-Scale feature extraction which is carried out from an image pyramid to detect defects of different sizes. Moreover, in order to make full use of the deep features, we use Variational AutoEncoder (VAE) to learn the feature distribution of normal samples for better reconstruction. Extensive experiments on the challenging and newly proposed MVTec AD dataset show that our method outperforms baselines.

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