.png){kind=icon}
返回
Boosting Small Object Detection via High-Frequency Feature Oriented Network
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2026.3652955.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Small Object Detection (SOD) aims to accurately identify and locate small objects in images. However, existing methods usually focus on exploring spatial domain features, neglecting high-frequency features that preserve fine-grained details such as texture and edge information. To overcome this limitation, we propose a High-Frequency Feature-Oriented Network (HFFO-Net). First, we introduce the Channel-wise Frequency Modulation Module (CFMM), which leverages the 2D Discrete Cosine Transform (DCT) to accentuate salient frequency components while mitigating noise interference. Second, we design a High-Frequency Oriented Module (HFOM), which utilizes the Channel Selection Branch (CSB) and Spatial Selection Branch (SSB) to highlight small objects in the channel and spatial region. Third, we introduce a Dual-Query Attention Fusion Mechanism (DQAFM), which reduces the semantic gap between spatial and frequency features and achieves better feature fusion through bidirectional cross-attention. Extensive experiments are implemented, and the corresponding results demonstrate that HFFO-Net excels at detecting small objects.
Keyword:
Small object detection
high-frequency features
feature enhanced
feature fusion
期刊
I
IF:
3.9
论文数:
824
被引数:
0
机构
引用论文
.png){kind=icon}
分享收藏
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection通过自适应训练样本选择弥合基于锚点和无锚检测之间的差距
OAAI
OAAI分享收藏分享收藏
查看更多内容