To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects.

Wire clamps and vibration-proof hammers are key components of high-voltage transmission lines. The wire clips and vibration-proof hammers detected in Unmanned Aerial Vehicle (UAV) power inspections suffer from small size, scarce edge information, and low recognition accuracy. To address these problems, this paper proposes a small object detection (SOD) model based on the YOLOv8n, called SOD-YOLO. Firstly, an extra small target detection layer was added to YOLOv8, which significantly improves the small target detection accuracy. In addition, in order to enhance the detection speed of the model, the RepVGG/RepConv ShuffleNet (RCS) and a OneShot Aggregation of the RCS (RCSOSA) module were introduced to replace the C2f module in the model backbone and neck shallow networks. Finally, to address the excessive focus on low-quality sample bounding boxes during model training, we introduced Wise-CIoU loss instead of CIoU loss, which improved the detection accuracy of the model. The experimental results indicate that SOD-YOLO achieved a mean average precision of 90.1%, surpassing the YOLOv8n baseline model by 7.5% while maintaining a model parameter count of 3.4 M; the inference speed reached 88.7 frames/s, which meets the requirement of real-time recognition.

The geo-localization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with non-shared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network (OS-FPI). Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network, and establishes additional connections between UAV and satellite feature maps in the feature fusion network. Additionally, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model, boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. As a one-stream network, our proposed model not only improves inference speed, but also reduces the number of parameters. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared to previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-meter accuracy, 64.17% improvement in 5-meter accuracy, and 37.43% improvement in 10-meter accuracy. Code and models are publicly available at https://github.com/cjhing/OS-FPI.

Traditional simultaneous localization and mapping (SLAM) methodologies predominantly rely on the assumption of a static environment. This constraint limits the applicability of most visual SLAM systems in various real-world scenarios. In this paper, we introduce a real-time semantic visual SLAM algorithm tailored for complex dynamic environments (DRV-SLAM). DRV-SLAM leverages image analysis to identify potential moving objects and determine their current motion states. By dynamically adjusting the rejection of unreliable dynamic feature points based on the proportion of potential moving objects in the environment, DRV-SLAM significantly enhances the system’s localization accuracy and robustness in complex dynamic environments. Additionally, DRV-SLAM employs a dense mapping approach that combines global downsampling and targeted object data enhancement. This method effectively reduces the memory footprint of dense point cloud maps, enabling DRV-SLAM to efficiently construct large-scale dense point cloud maps in diverse scenarios. Experimental results show that in a highly dynamic environment, the DRV-SLAM algorithm shows an order of magnitude performance improvement compared to the traditional ORB-SLAM2 algorithm. The performance index of absolute trajectory error is significantly improved by more than 98%, and DRV-SLAM is currently one of the most real-time, accurate and robust systems for dynamic scenes.

In high-voltage transmission lines, the tension tower needs to withstand the tension load of the overhead power line for a long time, which is prone to damage, and it is an important part of the inspection in the circuit inspection. In the modern circuit inspection process, operation and maintenance personnel mostly use unmanned aerial vehicles (UAVs) to photograph various parts of the tension tower, obtain inspection images, and manually classify and name the massive inspection images, which is low in accuracy and efficiency. Based on the above problems, this paper collects a large number of real-life UAV inspection images of various parts of a tension tower, and proposes the Stower-13 inspection image dataset, which is used to train the classification model to achieve automatic classification and naming of inspection images. Based on this dataset, this paper also proposes an improved MobileViT model, in which the Scale-Correlated Pyramid Convolution Block Attention Block (SCPCbam) module is introduced, which adds the Convolution Block Attention Module (CBAM) to the four branches of the original Scale-Correlated Pyramid Convolution (SCPC) module, so as to strengthen the ability of image multi-scale information extraction and improve the classification accuracy. This paper discusses a number of experiments on the model, and the experimental results show that the dataset proposed in this paper helps the model to understand the feature information. At the same time, the improved MobileViT model has a strong ability to extract image spatial feature information, the classification accuracy is higher than that of other models of the same type, so it is able to cope with a wide range of problems that arise in the course of practice, and it meets the practical needs of automatically naming transmission line inspection images.

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults.