Enhanced DINO for Cross-Domain Transmission Tower Detection Using Remote Sensing Images

Transmission towers are fundamental components of electric power networks. However, their structure, scale and background textures vary substantially across remote sensing images acquired from different geographic regions. These discrepancies often reduce the detection accuracy of a model trained in one region when it is applied to another region. This paper proposes an enhanced DINO-based framework for cross-domain transmission tower detection that incorporates three lightweight optimisation modules. First, a Query-level Objectness Gating (QOG) module adaptively reweights decoder queries by estimating per-query objectness scores, thereby suppressing background-dominated queries. Second, MPDIoU regression is used to improve the localisation accuracy of elongated transmission tower targets. Third, a Quality-aware Scoring Module (QSM) calibrates classification confidence using predicted localisation-quality logits, thereby reducing high-confidence false detections caused by poor box alignment. Experiments are conducted on two remote sensing image datasets from different geographic regions. Under the 10% target-domain annotation setting, the proposed method achieves a precision of 0.8947, a recall of 0.8199, an F1-score of 0.8556 and an mAP@0.5 of 0.8684, outperforming the original DINO baseline and mainstream detectors including YOLOv8, YOLOv9 and YOLOv11. The results demonstrate that the proposed framework provides an effective solution for robust cross-domain detection of slender transmission tower targets in remote sensing images.