WEI M J, GE Y H, YANG X, et al. Small-object detection in remote sensing images using multi-scale feature fusion [J]. Journal of Henan Polytechnic University (Natural Science) , 2025, 44(4): 40-47.

doi: 10.16186/j.cnki.1673-9787.2024070040

Received: 2024/07/09

Revised: 2024/09/10

Published: 2025/06/19

Small-object detection in remote sensing images using multi-scale feature fusion

Wei Mingjun1,2, Ge Yihui1, Yang Xuan1, Liu Yazhi1,2, Li Hui1

1. School of Artificial Intelligence， North China University of Science and Technology， Tangshan  063210， Hebei， China； 2. Hebei Key Laboratory of Industrial Intelligent Perception， Tangshan  063210， Hebei ， China

Abstract: Objectives Small objects in remote sensing images often lack sufficient discriminative features and are highly susceptible to interference from complex backgrounds， leading to frequent false and missed detections. To address this， a multi-scale feature fusion network is proposed to improve small-object detection accuracy in remote sensing images.  Methods A sparse attention-guided feature fusion module is first introduced into the medium-scale feature maps to enhance the network’s sensitivity to small objects and suppress background interference. Furthermore， to effectively integrate contextual information across different scales and improve localization accuracy， a multi-step dilated convolution fusion module is designed. This module applies multiple parallel convolutions with varying dilation rates to aggregate semantic information from features at multiple levels.  Results Extensive experiments conducted on the NWPU VHR-10， RSOD， and HRSID datasets demonstrate that the proposed method achieves significantly improved detection accuracy for small objects while maintaining or slightly enhancing performance on medium- and large-scale objects. The mAP@50 values on the NWPU VHR-10， RSOD， and HRSID datasets reached 63.1%， 96.92%， and 92.5%， respectively.   Conclusions These results demonstrate that the proposed method， which incorporates two multi-scale feature fusion strategies based on attention guidance and dilated convolution， can effectively enhance the detection accuracy of small objects in remote sensing targets. 

Key words: small-object detection; multi scale;remote sensing image; deep learning; feature fusion