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BRSTD: Bio-Inspired Remote Sensing Tiny Object Detection
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DOI：10.1109/TGRS.2024.3470900.png)
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Abstract
En 中文
In aerial images captured by drones or satellite remote sensing images, object information is weak and difficult to distinguish from the background, with significant variations in object sizes. Physiological research indicates that the visual system can select visual stimuli through an attention mechanism, focusing resources on processing important information while suppressing less important information. Inspired by biological vision, this article designs an object detection network, named bio-inspired remote sensing tiny object detection (BRSTD). Drawing inspiration from the parallel pathways in biological vision and the antagonistic receptive field properties of X, Y, and W cells, we designed the XYW-Conv module with antagonistic receptive fields. This module enhances the contrast between tiny objects and their surrounding information, effectively extracting tiny object information from images. To further improve the backbone network's ability to distinguish objects from the background, we designed the XYW-Attention and applied it to the designed Backbone. To better preserve tiny object information in the shallow feature layers and suppress large object and background information, we designed a feedback suppression attention module, top-down suppression attention (TDSA), at the connection between the neck and head parts, improving the model's performance. Experiments show that with only 1.8 M parameters, BRSTD achieved 10.4 in terms of APvt and 23.6 in terms of average precision (AP) on the AI-TOD dataset. It also performed excellently on other public remote sensing object datasets such as Visdrone and DOTA. This study not only advances remote sensing image object detection technology but also provides new ideas for the combined research of biological vision and computer vision (CV). The code will be open-sourced at https://github.com/huangyuesheng9/BRSTD.
Keywords:
Object detection
Remote sensing
Feature extraction
Detectors
Biology
Visualization
Accuracy
Physiology
Neck
Attention mechanisms
Aerial images
attention mechanism
bio-inspired
convolutional neural network (CNN)
tiny object detection
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8.6
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10.7W
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