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.

The number of samples is one of the key factors affecting the performance of deep learning-based detection networks. Aiming at the problem that the detection network is difficult to accurately detect the hot-spot fault targets under the condition of small samples, a photovoltaic hot-spot fault detection network based on progressive transfer learning and multi-scale feature fusion is proposed. First, a large number of artificial hot-spot samples are generated through the artificial model, and the mixed dataset containing real and artificial samples is constructed to improve the data diversity. On this basis, a pre-trained model based on artificial samples is established to learn the shallow features of hot-spot faults. Then, to fuse the multi-scale features and improve feature aggregation ability of detection network, a novel feature pyramid structure based on reparameterized generalized and multi-scale feature fusion (RepG-MSFF) is designed. Moreover, to balance the detection accuracy and speed, the spatial and channel reconstruction convolution is utilized to replace conventional convolution in the backbone network. Finally, to further accurately locate hot-spot targets, an adaptive threshold focal loss function is introduced. The experimental results indicate that in three different scenarios datasets, the detection accuracy can reach 87.9%, 88.6% and 87.7%, respectively, which is higher than that of other nine detection algorithms.

In view of the low levels of speed and precision associated with fault detection in transmission lines using traditional algorithms due to resource constraints, a transmission line fault target-detection method for YOLOv8 (You Only Look Once version 8) based on the Rep (Representational Pyramid) Visual Transformer and incorporating an ultra-lightweight module is proposed. First, the YOLOv8 detection network was built. In order to address the needs of feature redundancy and high levels of network computation, the Rep Visual Transformer module was introduced in the Neck part to integrate the pixel information associated with the entire image through its multi-head self-attention and enable the model to learn more global image features, thereby improving the computational speed of the model; then, a lightweight GSConv (Grouped and Separated Convolution, a combination of grouped convolution and separated convolution) convolution module was added to the Backbone and Neck to share computing resources among channels and reduce computing time and memory consumption, by which the computational cost and detection performance of the detection network were balanced, while the model remained lightweight and maintained its high precision. Secondly, the loss function Wise-IoU (Intelligent IOU) was introduced as the Bounding-Box Regression (BBR) loss function to optimize the predicted bounding boxes in these grid cells and shift them closer to the real target location, which reduced the harmful gradients caused by low-quality examples and further improved the detection precision of the algorithm. Finally, the algorithm was verified using a data set of 3500 images compiled by a power-supply inspection department over the past four years. The experimental results show that, compared with the seven classic and improved algorithms, the recall rate and average precision of the proposed algorithm were improved by 0.058 and 0.053, respectively, compared with the original YOLOv8 detection network; the floating-point operations per second decreased by 2.3; and the picture detection speed was increased to 114.9 FPS.

Under aerial imaging conditions, hot-spot faults exhibit angle distortion and small-scale characteristics in the infrared images, posing a challenge for precise localization in traditional detection algorithms. Therefore, a photovoltaic hot-spot fault detection algorithm based on angle perception and model migration is proposed, comprising a photovoltaic panel segmentation network and a hot-spot fault detection network. In the segmentation network, based on the DeepLabv3+ framework, a feature alignment module is designed to capture fine-grained photovoltaic panel regions. In the detection network, a cross-adaptive frequency Transformer module is constructed to enhance the detection accuracy of small-scale hot-spot faults, while a cross-scale shift network strengthens interactions between multi-scale features. Additionally, a convex hull feature adaptive method is introduced, which employs a joint optimization strategy to achieve precise localization of hot-spot faults with varying angles. To validate the algorithm’s superiority, it is compared with 13 detection algorithms. Experimental results show that our algorithm achieves a detection accuracy as high as 92.3% and can accurately detect hot-spot faults with diverse angles in complex environments.