Scene Recognition Model in Underground Mines Based on CNN-LSTM and Spatial-Temporal Attention Mechanism

Tianwei Zheng, C. Liu, Beizhan Liu, Mei Wang, Yuancheng Li, Pai Wang, Xuebin Qin, Yuan Guo
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引用次数: 1

Abstract

Based on the convolutional neural network (CNN) and long short-term memory neural network (LSTM), combined with data enhancement technology and spatial-temporal attention mechanism, a scene recognition model is established. In order to balance the difference in the amount of data between different samples, data enhancement technology based on video data samples is introduced. Aiming at improving the performance of the model, a spatial-temporal attention mechanism is used to improve the accuracy of scene recognition. The model sample scenes used in this paper include three types: fully mechanized coal face, coal mine roadway and mining machinery. The experimental verification shows that: compared with the traditional convolutional neural network, the accuracy of the CNN-LSTM model is improved by 2.136%, and the CNN-LSTM model with spatial-temporal attention mechanism is improved by 2.9210/0. The accuracy of the deep CNN-LSTM model with spatial-temporal attention mechanism is about 93.0630/0.
基于CNN-LSTM和时空注意机制的地下矿山场景识别模型
基于卷积神经网络(CNN)和长短期记忆神经网络(LSTM),结合数据增强技术和时空注意机制,建立了场景识别模型。为了平衡不同样本间数据量的差异,引入了基于视频数据样本的数据增强技术。为了提高模型的性能,采用了一种时空注意机制来提高场景识别的准确性。本文使用的模型样本场景包括三种类型:综采工作面、煤矿巷道和矿山机械。实验验证表明:与传统卷积神经网络相比,CNN-LSTM模型的准确率提高了2.136%,具有时空注意机制的CNN-LSTM模型的准确率提高了2.9210/0。考虑时空注意机制的深度CNN-LSTM模型的准确率约为93.030 /0。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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