Abstract
Detecting objects in aerial images confronts some significant challenges, including small size, dense and non-uniform distribution of objects over high-resolution images, which makes detection inefficient. Thus, in this paper, we proposed a small object detection algorithm based on a Spatial Laplacian Pyramid Attention and Multi-Scale Feature Enhancement in aerial images. Firstly, in order to improve the feature representation of ResNet-50 on small objects, we presented a novel Spatial Laplacian Pyramid Attention (SLPA) module, which is integrated after each stage of ResNet-50 to identify and emphasize important local regions. Secondly, to enhance the model's semantic understanding and features representation, we designed a Multi-Scale Feature Enhancement Module (MSFEM), which is incorporated into the lateral connections of C5 layer for building Feature Pyramid Network (FPN). Finally, the features representation quality of traditional feature pyramid network will be affected because the features are not aligned when the upper and lower layers are fused. In order to handle it, we utilized deformable convolutions to align the features in the fusion processing of the upper and lower levels of the Feature Pyramid Network, which can help enhance the model's ability to detect and recognize small objects. The extensive experimental results on two benchmark datasets: VisDrone and DOTA demonstrate that our improved model performs better for small object detection in aerial images compared to the original algorithm.
Abstract (translated)
在空中图像中检测对象面临一些重要的挑战，包括对象尺寸小、密度高且分布不均，这使得检测效率低下。因此，在本文中，我们提出了一种基于空间拉普拉斯金字塔注意（Spatial Laplacian Pyramid Attention）和多尺度特征增强的空中图像中小目标检测算法。 首先，为了提高ResNet-50在小对象上的特征表示能力，我们提出了一种新颖的空间拉普拉斯金字塔注意力模块(SLPA)，该模块被集成到ResNet-50的每个阶段之后，以识别并强调重要的局部区域。其次，为增强模型的语义理解和特征表示能力，我们设计了一个多尺度特征增强模块(MSFEM)，将其整合到C5层的横向连接中，用于构建特征金字塔网络（FPN）。最后，传统特征金字塔网络中的特征表示质量会受到影响，因为在上、下层融合时特征没有对齐。为了解决这一问题，我们在特征金字塔网络的上、下层级融合处理过程中使用了可变形卷积来对齐特征，这有助于增强模型检测和识别小对象的能力。 在VisDrone和DOTA两个基准数据集上的广泛实验结果表明，相较于原始算法，我们的改进型模型在空中图像中小目标检测方面表现更佳。
URL
https://arxiv.org/abs/2602.23031
