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.

The segmentation of ground-based cloud image is a crucial aspect of ground-based cloud observation, with significant implications for meteorological forecasting, photovoltaic power prediction, and other related tasks. At present, the proposed method of ground-based cloud image segmentation only separates cloud from the sky background without further classifying the cloud categories. Clouds have rich fine-grained semantic features, and different types of clouds have different effects on solar irradiance, which in turn has different effects on photovoltaic power. In this article, a fine-grained segmentation method for ground-based cloud images is proposed, which is based on an improved encoder–decoder structure named CloudFU-Net. First, a ground-based cloud image fine-grained segmentation dataset for photovoltaic power prediction is constructed, and the clouds are divided into five categories with different colors under the guidance of meteorologists. Second, selective kernel (SK) is introduced in the CloudFU-Net encoder to better capture cloud of different sizes. Then, a parallel dilated convolution model (PDCM) is proposed to segment small target clouds more accurately. Finally, a content-aware reassembly of features (CARAFE) is introduced into the CloudFU-Net decoder to replace the original interpolating upsampling to better recover fine-grained semantic features. Finally, the experimental results show that the proposed CloudFU-Net has the best segmentation performance compared with other segmentation models, with Miou reaching 61.9%, which can efficiently segment different cloud genera and lay a solid foundation for accurate prediction of photovoltaic power.

Under the background of continuous breakthroughs in the spatial resolution of satellite remote sensing technology, high-resolution remote sensing images have become a frontier data source for intelligent inspection research of power infrastructure. To address existing issues in remote sensing image application algorithms such as difficulties in power target feature extraction, low detection accuracy, and false positives/missed detections, this paper proposes the YOLOv9-GDV power tower detection algorithm specifically for power tower detection in high-resolution satellite remote sensing images. Firstly, under high-resolution imaging conditions where transmission tower features are prominent, a Global Pyramid Attention (GPA) mechanism is proposed. This mechanism enhances global representation capabilities, enabling the model to better understand object–background relationships and effectively integrate multi-scale spatial information, thereby improving detection accuracy and robustness. Secondly, a Diverse Branch Block (DBB) is embedded in the feature extraction–fusion module, which enriches the feature space by enhancing the representation capability of single-convolution operations, thereby improving model feature extraction performance without increasing inference time costs. Finally, the Variable Minimum Point Distance Intersection over Union (VMPDIoU) loss is proposed to optimize the model’s loss function. This method employs variable input parameters to directly calculate key point distances between predicted and ground-truth boxes, more accurately reflecting positional differences between detection results and reference targets, thus effectively improving the model’s mean Average Precision (mAP). On the Satellite Remote Sensing Power Tower Dataset (SRSPTD), the YOLOv9-GDV algorithm achieves an mAP of 80.2%, representing a 4.7% improvement over the baseline algorithm. On the multi-scene high-resolution power transmission tower dataset (GFTD), the algorithm obtains an mAP of 94.6%, showing a 2.3% improvement over the original model. The significant mAP improvements on both datasets validate the effectiveness and feasibility of the proposed method.