Work

EFSL-YOLO: An Improved Model for Small Object Detection in UAV Vision

Year: 2026

Type: article

Abstract: To address the challenges in UAV remote sensing imagery, such as small object size, dense occlusion and complex background interference, this paper proposes an enhanced small object detection algorithm based on an improved YOLOv13 model for drone applications in complex weather environments. First, an enhanced feature fusion attention network (EFFA-Net) is designed in the preprocessing stage to reduce image degradation and suppress the interference caused by smoke and haze. Then, in the backbone, a swish-gated convolution (SwiGLUConv) module is designed to adaptively expand the receptive field and enhance multi-scale feature extraction, which strengthens the representation of small targets while maintaining efficient computation. Furthermore, a locally enhanced multi-scale context fusion (LF-MSCF) module is integrated into the feature fusion neck of YOLO, combining multi-head self-attention, channel attention, and spatial attention to suppress background noise and redundant responses, thereby improving detection accuracy. Extensive experiments on the VisDrone-DET2019 dataset, UAVDT dataset, and HazyDet dataset demonstrate that the proposed algorithm outperforms other mainstream methods, showcasing excellent detection accuracy and robustness in complex UAV aerial scenarios.

Source: Drones

DOI: 10.3390/drones10040243

Authors: Meng Zhou, Shuke He, Chang Wang, Jing Wang

Institutions: North China University of Technology, Harbin Engineering University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: North China University of Technology, North China Institute of Science and Technology, National Key Research and Development Program of China