Center-Guided Network with Dynamic Attention for Transmission Tower Detection

Transmission tower detection in aerial images is the critical step for the inspection of power transmission equipment, which is essential for the stable operation of the power system. However, transmission towers in aerial images pose numerous challenges for object detection due to their multi-scale elongated shapes, large aspect ratios, and visually similar backgrounds. To address these problems, we propose the Center-Guided network with Dynamic Attention (CGDA) for detecting TTs from aerial images. Specifically, we apply ResNet and FPN as the feature extractor to extract high-quality and multi-scale features. To obtain more discriminative information, the dynamic attention mechanism is employed to dynamically fuse multi-scale feature maps and place more attention on the object regions. In addition, a two-stage detection head is proposed to employ a two-stage detection process to perform more accurate detection. Extensive experiments are conducted on a subset of the public TTPLA dataset. The results show that CGDA achieves competitive performance in detecting TTs, demonstrating the effectiveness of the proposed approach.