Vibration dampers and insulators are important components of transmission lines, and it is therefore important for the normal operation of transmission lines to detect defects in these components in a timely manner. In this paper, we provide an automatic detection method for component defects through patrolling inspection by an unmanned aerial vehicle (UAV). We constructed a dataset of vibration dampers and insulators (DVDI) on transmission lines in images obtained by the UAV. It is difficult to detect defects in vibration dampers and insulators from UAV images, as these components and their defective parts are very small parts of the images, and the components vary greatly in terms of their shape and color and are easily confused with the background. In view of this, we use the end-to-end coordinate attention and bidirectional feature pyramid network “you only look once” (BC-YOLO) to detect component defects. To make the network focus on the features of vibration dampers and insulators rather than the complex backgrounds, we added the coordinate attention (CA) module to YOLOv5. CA encodes each channel separately along the vertical and horizontal directions, which allows the attention module to simultaneously capture remote spatial interactions with precise location information and helps the network locate targets of interest more accurately. In the multiscale feature fusion stage, different input features have different resolutions, and their contributions to the fused output features are usually unequal. However, PANet treats each input feature equally and simply sums them up without distinction. In this paper, we replace the original PANet feature fusion framework in YOLOv5 with a bidirectional feature pyramid network (BiFPN). BiFPN introduces learnable weights to learn the importance of different features, which can make the network focus more on the feature mapping that contributes more to the output features. To verify the effectiveness of our method, we conducted a test in DVDI, and its mAP@0.5 reached 89.1%, a value 2.7% higher than for YOLOv5.

Timely and accurate monitoring of pine forest diseases is greatly important to pine forest disease prevention and forest protection. A method for unmanned aerial vehicle (UAV) remote sensing monitoring of pine forest diseases is proposed based on improved Mask region-based convolutional neural network (R-CNN) to solve the problem of low accuracy of the existing methods due to complex background and nonevident characteristics of pine forest diseases. The Mask R-CNN with multitask capabilities is utilized as backbone network to build the monitoring model. An improved multiscale receptive field block module is added to the Mask R-CNN to extract the detailed features of pine forest diseases and reduce the missed detection of pine forest diseases. Meanwhile, a multilevel fusion residual pyramid structure is adopted to integrate low-level and high-level features to obtain distinguishable features of pine forest diseases to reduce the misdetection and misidentification of complex backgrounds. In addition, the proposed method uses cutting mixing splicing method to construct training data set to increase the number of background objects in the training images and reduce the impact of complex backgrounds on the monitoring results. Experimental results show that the proposed method can monitor pine forest diseases more accurately than methods of SSD, Faster R-CNN, MaskScoring R-CNN, and the original Mask R-CNN. Compared with the method of backbone network, the precision of the proposed method has increased by 22.4%, the recall has increased by 3.5%, and the F1-score has increased by 14.4%.

The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the $mAP@0.5$ for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the $mAP@0.5$ was improved by a further 0.2%.

Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.