An Optimized Composite YOLO Model for Transmission Tower Detection in Satellite Optical Remote Sensing Imagery

Safe low-altitude flight requires precise perception of obstacles like widespread transmission towers. Traditional inspection is often costly and inefficient. While satellite remote sensing enables automated detection, transmission towers exhibit small scales, slender structures, and random orientations, causing feature loss and receptive field mismatch. This study constructs HRS-PTD, a multi-source, multi-resolution satellite optical dataset, and analyzes target morphology. We then propose an optimized composite YOLO model using a streamlined three-stage baseline with C3k2 and SPPF modules. To enhance small object feature reconstruction, CARAFE is integrated into the upsampling path for content-aware dynamic kernels. Furthermore, a direction-aware C_DCA module, incorporating deformable convolutions, utilizes multi-directional strip branches and adaptive attention to improve slender target representation. Ablation experiments show the model achieves 92.28% mAP, with precision and recall increasing by 1.53 and 12.12 percentage points over the baseline. Comparative experiments against representative classical detectors further demonstrate that the proposed model achieves superior overall performance in both detection accuracy and inference efficiency. Tests on Google Earth and Gaofen-7 imagery yield 88% and 76% accuracy, confirming real-world feasibility.