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.