E 2 -Former: An Edge-Enhanced Transformer for UAV-Based Small Object Detection

The widespread deployment of low-altitude drones in Internet of Things (IoT) applications, such as smart transportation and urban security, demands efficient, real-time visual perception on resource-constrained edge devices. A critical challenge in this domain is the precise detection and localization of small objects against complex backgrounds. Low-altitude UAV imagery is particularly challenging due to blurred small-target edges, weak anti-interference for medium and large targets, and fragmented multi-scale features. Existing convolutional and general-purpose Transformer-based detectors exhibit significant limitations in localization accuracy and robustness under these conditions. To address these issues, we proposeE2-Former, a novel object detection model based on the Detection Transformer (DETR) framework, designed for real-time performance on computational-edge devices. Our model integrates three core components: an Edge-Enhanced Backbone that augments sensitivity to contours while preserving deep semantics; a Polarized Dynamic Multi-Feature Fusion (PDMF) Transformer that leverages dual-path polarized attention and frequency-domain modulation to enhance local-global feature modeling and suppress background noise; and an Edge-aware Path Aggregation Network (E-PAN) that uses bidirectional gating and multi-level context interaction to resolve feature fragmentation and promote fine-grained, cross-scale integration. On three challenging low-altitude datasets—VisDrone2019, UAVDT, and CODrone—E2- Former achieves leadingAP50scores of 48.9%, 41.2%, and 33.0%, respectively, outperforming existing mainstream methods in both detection accuracy and robustness. Practical deployment and scene detection experiments further validate its impressive performance in real-world scenarios, establishing a new paradigm for building efficient and reliable low-altitude drone perception systems.