Indoor Scene Recognition Method Based on Multi-scale Feature and Attention Module

doi:10.3969/j.issn.1006-2475.2024.08.007

Abstract

Abstract: Scene recognition plays an important role in the task of visual information retrieval， segmentation and image/video understanding. With the development of deep learning theory， convolutional neural networks （CNN） greatly improve the ability of scene recognition by recognizing discriminative objects in images. In order to realize autonomous scene recognition for home service robots such as intelligent wheelchair beds， aiming at the condition of limited computing resources and memory requirements of mobile terminals or embedded devices， which leads to low scene recognition rate due to the single discriminative output from the network， an indoor scene recognition method based on multi-scale feature extraction and attention module is proposed. The method is based on MobileNetV2， which selects different branches from the network and extracts features at different scales. To focus on more discriminative features in the scene， the MRLA-Light attention module is added to the branches. The simulation results show that the accuracy is obviously improved， and the accuracy of tests on MIT Indoor 67 and Scene 15 scene datasets reaches 86.3% and 94.3% respectively， which is higher than the same type of networks.

Key words: , indoor scene recognition； lightweight network； attention module； feature extraction

CLC Number:

TP183

YUE Youjun1, 2, ZHANG Yuankun1, ZHAO Hui1, 2, WANG Hongjun1, 2. Indoor Scene Recognition Method Based on Multi-scale Feature and Attention Module[J]. Computer and Modernization, 2024, 0(08): 37-42.

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