Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1037009(2024)

Global-Sampling Spatial-Attention Module and its Application in Image Classification and Small Object Detection and Recognition

Jingyu Lu1,2,3, Haiyang Zhang1,2,3、*, Wenxin Wang1,2,3, and Changming Zhao1,2,3
Author Affiliations
  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081
  • 2Key Laboratory of Optoelectronic Imaging Technology and Systems, Ministry of Education, Beijing 100081
  • 3Key Laboratory of Information Photonics Technology, Ministry of Industry and Information Technology, Beijing 100081
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    The emergence and application of attention mechanisms have addressed some limitations of neural networks concerning the utilization of global information. However, common attention modules face issues with the receptive field being too small to focus on overall information. Moreover, existing global attention modules tend to incur high computational costs. To address these challenges, a lightweight, universal attention module, termed"global-sampling spatial-attention module", is introduced herein based on convolution, pooling, and comparison methods. This module relies on the comparison methods to derive spatial-attention maps for intermediate feature maps generated during deep network inference. Moreover, this module can be directly integrated into convolutional neural networks with minimal costs and can be end-to-end trained with the networks. The introduced module was primarily validated using a randomly selected subset of the ImageNet-1K dataset and a proprietary low-slow-small drone dataset. Experimental results show that compared with other modules, this module exhibits an improvement of approximately 1?3 percentage points in tasks related to image classification and small object detection and recognition. These findings underscore the efficacy of the proposed module and its applicability in small object detection.

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    Jingyu Lu, Haiyang Zhang, Wenxin Wang, Changming Zhao. Global-Sampling Spatial-Attention Module and its Application in Image Classification and Small Object Detection and Recognition[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1037009

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    Paper Information

    Category: Digital Image Processing

    Received: Aug. 18, 2023

    Accepted: Oct. 9, 2023

    Published Online: Apr. 29, 2024

    The Author Email: Zhang Haiyang (ocean@bit.edu.cn)

    DOI:10.3788/LOP231933

    CSTR:32186.14.LOP231933

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