Style transfer is adopted to synthesize appealing stylized images that preserve the structure of a content image but carry the pattern of a style image. Many recently proposed style transfer methods use only western oil paintings as style images to achieve image stylization. As a result, unnatural messy artistic effects are produced in stylized images when using these methods to directly transfer the patterns of traditional Chinese paintings, which are composed of plain colors and abstract objects. Moreover, most of them work only at the original image scale and thus ignore multiscale image information during training. In this paper, we present a novel effective multiscale style transfer method based on Laplacian pyramid decomposition and reconstruction, which can transfer unique patterns of Chinese paintings by learning different image features at different scales. In the first stage, the holistic patterns are transferred at low resolution by adopting a Style Transfer Base Network. Then, the details of the content and style are gradually enhanced at higher resolutions by a Detail Enhancement Network with an edge information selection (EIS) module in the second stage. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a comparison with some state-of-the-art style transfer methods. Datasets and codes are available at https://github.com/toby-katakuri/LP_StyleTransferNet.

Insulators play a crucial role in transmission lines. Insulators exposed to natural environments are prone to various malfunctions. These faults will seriously affect the safety and stability of the power grid system operation, so intelligent detection of insulator defects has become increasingly important. This paper presents an insulator defect detection model based on the improved MViTv2-T (Multiscale Vision Transformers Version 2 Tiny). The new model utilizes the sore penalty mechanism (SPM) cluster non-maximum suppression (NMS) algorithm instead of the batched non-maximum suppression (NMS) algorithm from the original model. Additionally, it introduces the stage query recollection method, which integrates high-level and low-level module queries within each stage, along with various experimentation on integration functions between the two. The experimental results indicate that the improved MViTv2-T model attains an mAP (mean average precision)@0.5:0.95 of 76.1 $\mathrm{\%}$, mAP@0.5 of 96.1 $\mathrm{\%}$, and mAR@0.5 of 97.2 $\mathrm{\%}$ in insulator defect detection. Compared to the original model, there is a 1.8 $\mathrm{\%}$ increase in mAP@0.5:0.95 and a 17 $\mathrm{\%}$ decrease in the detection error rate at an Intersection over Union (IoU) threshold of 0.5. Furthermore, when compared to standard two-stage detection models and YOLO series models, the improved MViTv2-T model also exhibits distinct performance advantages.

Insulators are crucial insulation components and structural supports in power grids, playing a vital role in the transmission lines. Due to temperature fluctuations, internal stress, or damage from hail, insulators are prone to injury. Automatic detection of damaged insulators faces challenges such as diverse types, small defect targets, and complex backgrounds and shapes. Most research for detecting insulator defects has focused on a single defect type or a specific material. However, the insulators in the grid's transmission lines have different colors and materials. Various insulator defects coexist, and the existing methods have difficulty meeting the practical application requirements. Current methods suffer from low detection accuracy and mAP0.5 cannot meet application requirements. This paper proposes an improved you only look once version 7 (YOLOv7) model for multi-type insulator defect detection. First, our model replaces the spatial pyramid pooling cross stage partial network (SPPCSPC) module with the receptive filed block (RFB) module to enhance the network's feature extraction capability. Second, a coordinate attention (CA) mechanism is introduced into the head part to enhance the network's feature representation ability and to improve detection accuracy. Third, a wise intersection over union (WIoU) loss function is employed to address the low-quality samples hindering model generalization during training, thereby improving the model's overall performance. The experimental results indicate that the proposed model exhibits enhancements across various performance metrics. Specifically, there is a 1.6% advancement in mAP_0.5, a corresponding 1.6% enhancement in mAP_0.5:0.95, a 1.3% elevation in precision, and a 1% increase in recall. Moreover, the model achieves parameter reduction by 3.2 million, leading to a decrease of 2.5 GFLOPS in computational cost. Notably, there is also an improvement of 2.81 milliseconds in single-image detection speed. This improved model can detect insulator defects for diverse materials, color insulators, and partial damage shapes in complex backgrounds.