Dynamic small object feature enhancement and detection for remote sensing images

Remote sensing images are extensively utilized for Earth observation; however, they often encounter challenges such as extremely small objects, cluttered backgrounds, and constraints related to edge computing. Current studies frequently face difficulties in achieving an optimal balance between recognition accuracy and efficiency to satisfy the requirements of various embedded devices. We propose DFE-DETR, a lightweight dynamic feature-enhanced detector that refines RT-DETR for remote-sensing scenes. The model relies on three core modules: the Sparse Attention Enhancement Module (SAEM) that prunes irrelevant tokens via Top-k scoring to cut compute, the Multi-scale Convolutional Attention Enhancement Module (MSCAEM) that employs multi-branch depth-wise strip convolutions to sharpen slender targets, and the Deformable Large Kernel Convolution Module (DLKCM) that uses adaptive sampling to locate irregular objects. Experiments demonstrate that this framework achieves a significant performance breakthrough on the VisDrone2019 dataset with only 23.6 M parameters, reaching 47.34% mAP@0.5 and 65.8 FPS. On the SIMD dataset, it attains 82.79% mAP@0.5 and 74.6 FPS. Meanwhile, on the RSOD and NWPU VHR-10 datasets, it achieves mAP@0.5 scores of 96.58% and 93.28%, respectively. Ablation experiments validate the synergistic effects of the modules, and visualization heatmaps indicate that the proposed model provides the most comprehensive target perception, accurately delineating target contours. In comparative experiments, the model outperformed mainstream YOLO series and specialized UAV detection models, offering a new high-precision real-time detection solution for embedded scenarios such as disaster response and traffic monitoring.