Small Object Detection Model with Spatial Laplacian Pyramid Attention and Multi-Scale Features Enhancement in Aerial Images

2026-02-26

AI Summary

This work addresses the performance bottleneck in detecting small objects in aerial images, which arises from their diminutive size, dense clustering, and uneven distribution. To tackle this challenge, the authors propose a detection framework that integrates a Spatial Laplacian Pyramid Attention (SLPA) mechanism with a Multi-Scale Feature Enhancement Module (MSFEM). The SLPA is embedded within a Feature Pyramid Network to enhance the representation of small-object features, while deformable convolutions are employed to achieve precise alignment between high- and low-level features. Built upon a ResNet-50 backbone, the complete detection pipeline demonstrates significant improvements over existing methods on the VisDrone and DOTA benchmark datasets, achieving notable gains in both accuracy and robustness for small object detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• small object detection
• aerial images
• feature representation
• multi-scale features
• object distribution

Innovation

• Spatial Laplacian Pyramid Attention
• Multi-Scale Feature Enhancement
• Deformable Convolution
• Feature Pyramid Network
• Small Object Detection