LSKF-YOLO: Large Selective Kernel Feature Fusion Network for Power Tower Detection in High-Resolution Satellite Remote Sensing Images

Abstract

With the rapid development of high-resolution satellite remote sensing observation technology, power tower detection based on satellite remote sensing images has become a key research focus for power intelligent inspection. However, the performance of power tower detection in satellite remote sensing images needs improvement due to complex backgrounds, and small and nonuniform target sizes. To address this, this article first constructs a multiscene high-resolution satellite remote sensing power tower dataset and then proposes the large selective kernel feature fusion (LSKF)-you only look once (YOLO) network for high-resolution satellite remote sensing images. This network primarily consists of a large spatial kernel selective attention fusion module and a multiscale feature alignment fusion (MFAF) structure. The large spatial selective kernel mechanism (LSKM) is improved by using the attentional feature fusion module, which provides richer feature information for accurately locating the position of the power tower. The MFAF structure effectively utilizes low-level semantic information, mitigates feature ambiguity in deeper network layers, and enables multiscale feature fusion of power towers within complex backgrounds. In addition, the introduction of minimum point distance-IoU (MPDIoU) enhances complete-IoU (CIoU), further improving the model’s performance. The results demonstrate that the $F1$ score and mAP0.5 of the LSKF-YOLO network reach 0.764% and 77.47%, respectively. Compared with other deep learning-based satellite remote sensing power tower inspection methods, the LSKF-YOLO network significantly enhances detection accuracy and provides crucial technical support for intelligent inspection of power lines via satellite remote sensing.