Dr. Zheng Wang Profile

Dr. Zheng Wang

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SPIE Journal Paper | 16 January 2025

High-accuracy detection method for miner action recognition with shuffle attention SlowFast networks

Bo Yang, Fangfang Xin, Zheng Wang, Xinyu He

JEI, Vol. 34, Issue 01, 013012, (January 2025) https://doi.org/10.1117/12.10.1117/1.JEI.34.1.013012

KEYWORDS: Video, Action recognition, Mining, Video acceleration, Education and training, Visualization, Feature extraction, Transformers, Deep learning, Optical flow

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One of the principal causes of mining accidents is the unsafe conduct of miners. The monitoring of miners’ unsafe actions and the timely implementation of corrective measures can effectively prevent a considerable number of preventable accidents. The SlowFast network represents a representative network for video action recognition. In comparison with conventional two-stream networks, the SlowFast networks avoid the extraction of optical flow information. Nevertheless, the quality of the imaging of coal mine images is inferior to that of regular images, exhibiting deficiencies such as inadequate illumination and the loss of fine details. Furthermore, the original network was incapable of effectively integrating horizontal information, resulting in a further reduction in accuracy when testing for coal miners. To address these issues, a shuffle attention SlowFast network for video action recognition has been proposed. A lightweight yet efficient shuffle attention module is employed to accurately focus on all the relevant elements of the lateral connections while also focusing action information through the attention mechanism. To address the issue of data imbalance, a linear combination of polynomial functions, termed PolyLoss, is employed. The proposed architecture was evaluated on three datasets: the public UCF-101 dataset, the public HMDB-51 dataset, and a self-constructed dataset of miner actions. The experimental results demonstrated that the proposed method exhibited superior performance compared with existing state-of-the-art methods for both regular images and images of underground mines.

Proceedings Article | 12 October 2020 Paper

Coal dust image recognition based on improved VGG convolution network

Dongyan Li, Zheng Wang, Helin Zhang

Proceedings Volume 11574, 115740O (2020) https://doi.org/10.1117/12.2576974

KEYWORDS: Particles, Image segmentation, Convolution, Image processing, Neural networks, Feature extraction, Convolutional neural networks

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In views of the problems of particle contour overlap and unclear texture detection of coal dust explosion, this paper proposed a method based on improved deep learning vgg-16 convolutional neural network model to obtain the feature information of particle image. Based on the vgg-16 network model, the SELayer is added after sampling under the first two convolutional layers to compress and extract the deep features of the particle image. The original SoftMax classifier was replaced by a binary classifier to optimize the model parameter structure. The weight parameters of convolution layer and pooling layer in the pre-training model were shared by micro-migration learning to speed up the operation. Samples were randomly selected from the constructed coal dust image as training set and test set to test the performance indexes of the model. The experimental results show that the proposed method has 2% promoted of recognition accuracy to the conventional methods, and achieved a lower loss value, which can meet the detection requirements of coal dust particle image.

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