Insulator parallel gaps in a transmission line protect the insulator from arc burn and provide protection from lightning. Detection of insulator parallel-gap faults based on aerial inspection images of transmission lines may be hindered by various factors, such as complex background, scale distortion, and occlusion. In this study, we propose a prior knowledge transfer and attention mechanism network (PKAMNet) to detect faulty insulator parallel gaps. First, the capability of PKAMNet to learn the features of different types of parallel-gap faults is improved by constructing a prior knowledge transfer model based on visual saliency. Owing to the difficulty of effectively expressing the features of the fault target due to the deviation of the shooting angle and complex background of the inspection image, we enhance the feature expression ability of the fault area by embedding the coordinate attention block. Additionally, the parameters of the detection network are simplified, and the modules are optimized to improve detection efficiency. Comparison results on 3-year aerial inspection videos of 500 kV high-voltage transmission lines show that PKAMNet has a high detection accuracy and can ameliorate the phenomenon of missed and erroneous detection caused by insufficient expression of parallel-gap faults.

Photovoltaic (PV) power generation technology is an effective approach for alleviating the current energy crisis. However, PV modules are prone to hot-spot faults and shorter service lives because of harsh long-term operating conditions. Therefore, in this study, we propose a bi-branch collaborative training algorithm based on knowledge distillation for PV hot-spot detection systems, focusing on the accuracy and computational efficiency of the algorithm. The proposed approach uses a knowledge distillation model based on a “teacher and student” collaborative training structure to improve detection accuracy while ensuring inference speed. The anchor-free YOLOX detection algorithm is used as the student network to reduce the number of network parameters. This includes a decoupled prediction head designed to improve detection performance. The teacher network first uses a HorNet-based cross-stage partial network (CSPHN)-based backbone network to enhance feature representation ability by capturing higher order spatial interactions. Second, a bi-branch multilevel feature adaptive fusion (BiMAF) module is designed to fuse multiscale features from both global and local perspectives in a parallel manner. Third, a shift contextual transformer (SCT)-based prediction head is adopted to enhance the long-range interaction between cross-scale features, thereby significantly improving the accuracy of the detection algorithm in dense scenes. Finally, comparative experiments are conducted on 13 classical detection algorithms to verify the effectiveness of the proposed detection network. The experimental results demonstrated that the proposed system can achieve fast and accurate detection of multiscale hot-spot faults under various harsh conditions, achieving an AP50 metric of 82.2%.

To address the problem of traditional pedestrian detection methods being subject to random interference from the external environment and insufficient utilization of pedestrian feature information, a novel multi-modal pedestrian detection network called MDFOaNet is proposed. The proposed detection network consists of two key components: a marine predator-based multi-scale image fusion module and a pedestrian detection module with an enhanced target visual accuracy attention model. In the fusion module, a contrast-based layered enhancement method for infrared images and a sharpness-based enhancement method for visible images are proposed for the problem of blurred pedestrian features in images. Moreover, to control the trade-off between fusion sub-layers, a dynamic image reconstruction model that relies on adaptive optimization based on marine predators is designed. Meanwhile, in the pedestrian detection module, to pay more attention to the main information in image and ignore some irrelevant information, an EVAM attention model is designed under the framework of YOLOv5s detection network, which improves the saliency of pedestrian targets and suppress the background interference. The experimental results show that compared with nine typical algorithms, the proposed algorithm can achieve accurate detection of multi-scale targets in complex environments, and is significantly superior to the compared detection algorithms in both subjective and objective evaluation indicators. The mAP and recall rates of the proposed network can reach 88.9% and 87.5%, respectively.

To address the problems of edge blur and weak detail resolution when fusing infrared and visible images with traditional methods, a novel image fusion approach based on fast super-resolution convolutional neural network and anisotropic diffusion is proposed. The proposed method first employs super-resolution reconstruction based on fast convolutional neural networks to get high-resolution infrared images. Second, the visible and high-resolution infrared images are anisotropically diffused to obtain the corresponding base layers and detail layers. Next, the fusion of the detail layer is performed through Karhunen-Loeve transform, and an adaptive saliency module is constructed to fuse the base layer. Finally, the fusion result is obtained by applying linear reconstruction on the fused detail layer and base layer. Compared with other algorithms, the experimental results show that the proposed algorithm not only has better objective evaluation indexes but also can better retain the details of the original image.