Aerial images have complex backgrounds, small targets, and overlapping targets, resulting in low accuracy of intelligent detection of overhead line insulators. This paper proposes an improved algorithm for insulator breakage detection based on YOLOv5: The ECA-Net (Efficient Channel Attention Network) attention mechanism is integrated into its backbone feature extraction layer, and the effective distinction between background and target is achieved by increasing the weight of important channels. A bidirectional feature pyramid network is added to the feature fusion layer, and large-scale images with more original information are combined to effectively retain small target features. Incorporating a flexible detection frame selection algorithm Soft-NMS (Soft Non-Maximum Suppression) into the prediction layer to re-screen the target frame, thereby reducing the probability of mistaken deletion of overlapping targets. The effectiveness of the improved YOLOv5 algorithm is verified in the actual aerial image dataset, and the results show that the mean Average Precision (mAP) of the improved algorithm is 95.02% and the detection speed FPS (Frames Per Second) can reach 49.4 frames/s, which meets the real-time and accuracy requirements of engineering applications.

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.

The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.

To further improve the accuracy and speed of UAV inspection of transmission line insulator defects, this paper proposes an insulator detection and defect identification algorithm based on YOLOv4, which is called DSMH-YOLOv4. In the feature extraction network of the YOLOv4 model, the improved algorithm improves the residual edges of the residual structure based on feature reuse and designs the backbone network D-CSPDarknet53, which greatly reduces the number of parameters and computation of the model. The SA-Net (Shuffle Attention Neural Networks) attention model is embedded in the feature fusion network to strengthen the attention of target features and improve the weight of the target. Multi-head output is added to the output layer to improve the ability of the model to recognize the small target of insulator damage. The experimental results show that the number of parameters of the improved algorithm model is only 25.98% of that of the original model, and the mAP (mean Average Precision) of the insulator and defect is increased from 92.44% to 96.14%, which provides an effective way for the implementation of edge end algorithm deployment.

Power line segmentation is very important to ensure the safe and stable operation of unmanned aerial vehicles in intelligent power line inspection. Although the power line segmentation algorithm based on deep learning has made some progress, it is still quite difficult to achieve accurate power line segmentation due to the complex and changeable background of aerial power line images and the small power line targets, and the existing segmentation models is too large and not suitable for edge deployment. This paper proposes a lightweight power line segmentation algorithm—G-UNets. The algorithm uses the improved U-Net of Lei Yang et al. (2022) as the basic network (Y-UNet). The encoder part combines traditional convolution with Ghost bottleneck to extract features and adopts a multi-scale input fusion strategy to reduce information loss. While ensuring the segmentation accuracy, the amount of Y-UNet parameters is significantly reduced; Shuffle Attention (SA) with fewer parameters is introduced in the decoding stage to improve the model segmentation accuracy; at the same time, in order to further alleviate the impact of the imbalanced distribution of positive and negative samples on the segmentation accuracy, a weighted hybrid loss function fused with Focal loss and Dice loss is constructed. The experimental results show that the number of parameters of the G-UNets algorithm is only about 26.55% of that of Y-UNet, and the F1-Score and IoU values both surpass those of Y-UNet, reaching 89.24% and 82.98%, respectively. G-UNets can greatly reduce the number of network parameters while ensuring the accuracy of the model, providing an effective way for the power line segmentation algorithm to be applied to resource-constrained edge devices such as drones.

In the context of difficulty in detection problems and the limited computing resources of various fault scales in aerial images of transmission line UAV inspections, this paper proposes a TD-YOLO algorithm (YOLO for transmission detection). Firstly, the Ghost module is used to lighten the model’s feature extraction network and prediction network, significantly reducing the number of parameters and the computational effort of the model. Secondly, the spatial and channel attention mechanism scSE (concurrent spatial and channel squeeze and channel excitation) is embedded into the feature fusion network, with PA-Net (path aggregation network) to construct a feature-balanced network, using channel weights and spatial weights as guides to achieving the balancing of multi-level and multi-scale features in the network, significantly improving the detection capability under the coexistence of multiple targets of different categories. Thirdly, a loss function, NWD (normalized Wasserstein distance), is introduced to enhance the detection of small targets, and the fusion ratio of NWD and CIoU is optimized to further compensate for the loss of accuracy caused by the lightweightedness of the model. Finally, a typical fault dataset of transmission lines is built using UAV inspection images for training and testing. The experimental results show that the TD-YOLO algorithm proposed in this article compresses 74.79% of the number of parameters and 66.92% of the calculation amount compared to YOLOv7-Tiny and increases the mAP (mean average precision) by 0.71%. The TD-YOLO was deployed into Jetson Xavier NX to simulate the UAV inspection process and was run at 23.5 FPS with good results. This study offers a reference for power line inspection and provides a possible way to deploy edge computing devices on unmanned aerial vehicles.