Insulators are critical electric components in transmission lines. Recognizing insulators and detecting the faults timely and accurately is essential for maintaining the safety and stability of transmission lines. Traditional methods have low accuracy and poor applicability in insulator recognition and fault detection. An insulator recognition and fault detection model was proposed in the article aiming at improving the insulator recognition and fault detection accuracy. First, based on the faster region convolutional neural network (RCNN), the feature pyramid networks (FPNs) were used to improve the Faster RCNN model and locate the insulators with complex background image. Then, the target area was clipped to remove the redundant background noise, and the hue, saturation, and value (HSV) color space adaptive threshold algorithm was applied for image segmentation due to the influence of light, background noise, and shooting angle. Finally, line detection, image rotation, and vertical projection were used to finish the insulator fault detection. The experimental results show that the proposed insulator recognition and fault detection model can recognize the insulators and detect fault types with better accuracy and achieve a mean average precision (mAP) of 90.8% for glass insulators and 91.7% for composite insulators on the testing dataset. Additionally, the proposed method meets the intelligent inspection of insulator faults in transmission lines and has good engineering application value.

The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.

Because insulators provide electrical insulation and mechanical support for electric transmission lines, these components are of paramount importance to safe and reliable operations of power systems. However, insulators are often considered to be prone to different faults, e.g., bunch-drop, which demands a novel solution for accurate fault-detection and fault-location. Current research efforts have primarily focused on the bunch-drop fault of glass insulators, and the study of ceramic insulators has not been reported to date. To this end, this paper proposes an algorithmic solution for bunch-drop fault detection for both glass and ceramic insulators based on spatial morphological features, which can be integrated into an unmanned aerial vehicle (UAV)-based inspection system. Color models can be established based on the unique color features of both glass and ceramic insulators. Next, the target areas of the insulators can be identified according to the color determination combined with the insulator’s spatial features. The target area is morphologically processed to highlight the fault location, and the rules are established based on the spatial feature differences between the insulators with and without faults. Consequently, the fault location can be accurately identified, and the coordinates can be determined. The performance of the proposed solution is evaluated in comparison with existing solutions. The numerical results demonstrate that the proposed solution can detect bunch-drop faults of insulators with a better than average detection rate. In addition, the performance is assessed and validated in terms of robustness and real-time performance.

Bolts are the most numerous fasteners in transmission lines and are prone to losing their split pins. How to realize the automatic pin-missing defect detection for bolts in transmission lines so as to achieve timely and efficient trouble shooting is a difficult problem and the long-term research target of power systems. In this paper, an automatic detection model called Automatic Visual Shape Clustering Network (AVSCNet) for pin-missing defect is constructed. Firstly, an unsupervised clustering method for the visual shapes of bolts is proposed and applied to construct a defect detection model which can learn the difference of visual shape. Next, three deep convolutional neural network optimization methods are used in the model: the feature enhancement, feature fusion and region feature extraction. The defect detection results are obtained by applying the regression calculation and classification to the regional features. In this paper, the object detection model of different networks is used to test the dataset of pin-missing defect constructed by the aerial images of transmission lines from multiple locations, and it is evaluated by various indicators and is fully verified. The results show that our method can achieve considerably satisfactory detection effect.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network (CNN) methods on the Adience, MORPH Album 2, FG-NET and 15/16LAP datasets.

In the detection of insulator defects on transmission lines, the detection precision is still not ideal, primarily attributed to the significant variation in target scale and complex image backgrounds. We propose the multiscale channel information (MCI)-global-local attention (GLA), a plug-in designed for YOLO series models, featuring two modules: the MCI extraction module and the GLA based on context information module (GLA-CI). MCI comprehensively extracts and utilizes multiscale feature map information, while GLA-CI captures both global context information and local spatial details, thereby augmenting the learning capability of networks. Experimental results indicate that the MCI-GLA plug-in improves the average precision (AP) of YOLOv4 to YOLOv8 models in detecting insulator breakage defects by 7.3%, 4.6%, 4.5%, 4.0%, and 5.3%, respectively. In particular, YOLOv7+MCI-GLA exhibits superior precision and inference time compared to other methods on self-constructed and public datasets. The code for this article can be found at https://github.com/falian0527/MCI-GLA .