Efficient vision transformers with edge enhancement for robust small target detection in drone-based remote sensing - Zhu, Xuguang; Zhang, Zhizhao (Frontiers in Remote Sensing, 2025)
Small object detection in UAV remote sensing imagery faces significant challenges due to scale variations, background clutter, and real-time processing requirements. This study proposes a lightweight transformer-based detector, MLD-DETR, which enhances detection performance in complex scenarios through multi-scale edge enhancement and hierarchical attention mechanisms. First, a Multi-Scale Edge Enhancement Fusion (MSEEF) module is designed, integrating adaptive pooling and edge-aware convolution to preserve target boundary details while enabling cross-scale feature interaction. Second, a Layered Attention Fusion (LAF) mechanism is developed, leveraging spatial depth-wise convolution and omnidirectional kernel feature fusion to improve hierarchical localization capability for densely occluded targets. Furthermore, a Dynamic Positional Encoding (DPE) module replaces traditional fixed positional embeddings, enhancing spatial perception accuracy under complex geometric perspectives through learnable spatial adapters. Combined with an Inner Generalized Intersection-over-Union (Inner-GIoU) loss function to optimize bounding box geometric consistency, MLD-DETR achieves 36.7% AP50% and 14.5% APs on the VisDrone2019 dataset, outperforming the baseline RT-DETR by 3.2% and 1.8% in accuracy while achieving 20% parameter reduction and maintaining computational efficiency suitable for UAV platforms equipped with modern edge computing hardware. Experimental results demonstrate the algorithm’s superior performance in UAV remote sensing applications such as crop disease monitoring and traffic congestion detection, offering an efficient solution for real-time edge-device deployment.