SABR-YOLO: A Lightweight Detector for Power Transmission Tower Detection in Remote Sensing Image

The detection of transmission towers in remote sensing images presents significant challenges due to insufficient feature representation and significant object size variations, particularly at low spatial resolutions where robust feature extraction and fusion capabilities are essential. To address these limitations, we propose YOLO with Sobel attention and dynamic branch reweighting (SABR-YOLO), an efficient detector incorporating two innovative lightweight modules: the Sobel-enhanced convolutional block attention module for enhanced local perception through edge-aware feature learning, and the branch reweighting feature pyramid network (BRFPN) for improved multiscale feature fusion with minimal computational overhead. To verify the effectiveness of SABR-YOLO, we construct a new dataset of transmission tower object detection (TTOD), which includes transmission towers of various types, shapes, and spatial resolutions. To evaluate its performance on other multiscale object detection tasks, we performed experiments on the public datasets of remote sensing object detection (RSOD) and super tiny object detection for remote sensing (RS-STOD). The experimental results show that SABR-YOLO achieves mAP<inline-formula><tex-math notation="LaTeX">$_{50}$</tex-math></inline-formula> scores of 0.965 on the RSOD dataset, 0.872 on the RS-STOD dataset, and 0.756 on TTOD dataset, outperforming several baseline models and state-of-the-art methods.