AF-YOLO: Asymptotic Feature Extraction and Fusion for Aerial Object Detection

作者：Lve Huang, Xiaowei Yu, Hua-biao Yan, Libo Huang, Zhulin An, Yongjun Xu · 发表于：IEEE Transactions on Circuits and Systems for Video Technology · 年份：2025 · DOI：10.1109/tcsvt.2025.3595740 · 被引用次数：5 · 研究领域：Infrared Target Detection Methodologies、Robotics and Sensor-Based Localization、Advanced Image and Video Retrieval Techniques

Aerial object detection plays a vital role in applications such as natural disaster prevention and urban traffic management, thanks to its ability to handle wide coverage areas and diverse objects. As a leading method for this task, You Only Look Once (YOLO) leverages multi-scale feature extraction to detect objects of various sizes. However, most YOLO-based methods focus on feature extraction and fusion from adjacent scales, neglecting the potential collaboration between non-adjacent scales. This limitation leads to redundant parameters and suboptimal detection performance. To address these issues, this paper proposes AF-YOLO (Asymptotic Feature Extraction and Fusion YOLO), a novel approach tailored for aerial object detection. AF-YOLO introduces two lightweight modules: SCC2f and PAFFN. SCC2f, an optimized version of cross-stage partial bottleneck with spatial and channel reconstruction convolution layers, reduces redundancy and enables efficient multi-scale feature extraction. PAFFN, a parallel asymptotic feature fusion network, facilitates enhanced interaction and fusion of non-adjacent scale features. Additionally, AF-YOLO incorporates a P2 layer to improve small object detection and removes YOLO’s P5 layer for a more lightweight design, specifically optimized for aerial detection tasks. Experimental results demonstrate AF-YOLO’s significant improvements across multiple benchmarks: on the VisDrone dataset, it achieves a 6.1% higher mAP0.5compared to recent baselines whil...