TY  - EJOU
AU  - Wang, Heng 
AU  - Li, Shichao 
AU  - Xu, Long 
AU  - Wang, Chuqiao 
AU  - Feng, Yanzhou 
AU  - Zhou, Zou 

TI  - EMW-YOLO: A Detail-Preserving and Multi-Scale Fusion Detector for Remote Sensing Small Object Detection
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - The inherent challenges of small objects in remote sensing imagery encompass the degradation of fine-grained spatial details throughout the downsampling stages, semantic inconsistency during multi-level feature fusion, along with unreliable localization caused by noisy samples. To address these issues, this paper proposes an efficient small-object detector termed EMW-YOLO. An Efficient Down-sampling (EDS) module is introduced to preserve fine-grained spatial information and enhance feature representation during feature extraction through spatial rearrangement and cross-dimensional attention. A Multi-Scale Fusion and Enhancement (MSFE) architecture is further developed to improve semantic consistency across feature levels by combining local enhancement with global feature alignment. In addition, the WIoU v3 loss function is also incorporated to suppress outlier samples and enhance the robustness of regression for small objects. Experimental results on the VisDrone2021 dataset demonstrate that EMW-YOLO achieves absolute improvements of 6.2%, 6.9%, and 7.9% over YOLOv8n in precision, recall, and mAP0.5, respectively. Further evaluation on the NWPU VHR-10 and RSOD datasets confirms that the proposed method exhibits strong generalization capabilities.
KW  - Small object detection; attention mechanism; multi-scale fusion; remote sensing images; feature representation

DO  - 10.32604/cmc.2026.083432