Reviewerly/exHarmony huggingface dataset:
An optical character recognition algorithm for documents with cascading structures

Document recognition is an important area of optical character recognition(OCR) technology. In this paper, an OCR recognition algorithm for document based on the three-stage text recognition approach is proposed, and is tested with a self-built dataset. The experimental results show that the recognition accuracy of the proposed algorithm is improved by 5.8% compared to the PP OCR V3 algorithm.
UGAN-GSAM-IT: Unsupervised Generative Adversarial Network with Generative Self-Attention Method for Image Translation

The unsupervised image translation is the core task of computer image processing, spanning a variety of real-world application scenarios, and constitutes an important branch of computer vision research orientations. In image translation tasks with significant geometric variations, conventional image translation algorithms have led to the generation of low-quality images due to unstable training. To address this matter, this paper proposes an unsupervised image translation method that combines a Self-Attention module. Specifically, first, the proposed model constructs the Generative Self-Attention Module and embeds it between the residual blocks and up-sampling in the generator, while inserting it in the discriminator before down-sampling. Then, the number of residual blocks is modified. Finally, a residual attention mechanism is introduced in the discriminator, which helps to propagate the features further. In particular, the model enhances the ability of the generator to extract deeper features at the central pixels, further improving the ability to transform the spatial details of the image and guiding the generator to generate more realistic images. Experimental results demonstrate that the proposed model has preferable superiority and performance to the existing unsupervised image translation models.
Salient Object Detection via Hybrid Encoder and Feature Feedback Fusion Mechanism

Deep encoder-decoder networks have been adopted for saliency detection and achieved the state-of-the-art performance. However, most existing saliency methods usually fail to process images with complex background and small objects. In this paper, we propose and apply a new method for saliency detection using hybrid encoder and feature feedback fusion mechanism. The proposed network architecture is composed of hybrid encoder based feature extraction module, feature feedback fusion mechanism based decoder module and multi observation point supervised learning module. In the feature extraction module, CNN encoder and transformer encoder are used to extract the local and global features of the image respectively. The decoder module optimizes the salient features of each layer by effectively fusing the visual features of the two layers or three layers. The multi observation point supervised learning module sets multiple observation points at each layer of the decoder, and each observation point considers the binary cross entropy loss and intersection over union loss. A large number of experimental results on five public data sets show that the method proposed in this paper is significantly better than other methods in different evaluation metrics.
A new style transfer method based on color prioritization

Style transfer has been widely used and developed in cultural entertainment industry. However, the traditional neural style transfer cannot perfectly transfer the color style of artistic images. To address this critical issue, we propose a color-first realtime style transfer framework. On the one hand, we ensure adequate transmission of color by giving priority to color transfer; On the other hand, we design a color perceptual loss to reduce the loss of color style. Qualitative and quantitative experiments demonstrate that compared with other state-of-the-art methods, our framework can effectively transfer the color style of artistic images with higher accuracy and richness, as well as good visual perception.
Path planning of power line inspection based on DDPG for obstacle avoidance with UAV

Obstacle avoidance is an important application in the path planning for Unmanned Aerial Vehicle power line inspections. Only by avoiding obstacles during the inspection process can the UAV complete the inspection task well. After analysis we propose to use reinforcement learning algorithms to solve the obstacle avoidance problem in UAV inspection. We use a depth-deterministic policy gradient algorithm to implement obstacle avoidance for UAV inspection and combine it with an artificial potential field method to speed up training. We validated the DDPG algorithm in a simulated environment with simulated obstacles, and the UAV was able to avoid the obstacles well and reach the target point.
Power Text Data Preprocessing of Power Grid Infrastructure Project based on Skip-gram Model

The power grid infrastructure project is a large-scale and long-period instance which often involves various subjects. It would produce a large amount of data, serving as an important original data source of the operating maintenance and asset management systems in power supply enterprises. However, the manpower analysis fails to deal with unstructured natural text language data as well as nonstandard semi-structured tabular data. To address this issue, the deep analysis on the data with different forms is first conducted based on the power grid infrastructure project. Then, a data cleaning technique is used to eliminate the noise in the original low-quality data. Finally, a skip-gram model is built to convert the text data into a word embedding vector form. The well-preprocessed data contains contextual semantic information which is more suitable for data mining. Extensive simulation experiments clearly demonstrate the effectiveness of the proposed method.
DWA-Watermarking Embedding Mechanism for Deep Learning Models

The black-box watermarking for deep learning models utilizes an overfitting trigger set to embed watermarking, and researchers focus on constructing the trigger set. However, this article does not focus on the form of trigger set construction, but rather on the training process of the trigger set. The proposed DWA-Watermarking embedding mechanism takes this into account. The idea of multitask learning is introduced in the watermarking embedding process, and the dynamic weight averaging algorithm is used for the first time to balance the training of the watermarking embedding task and the original task during the training process by dynamically adjusting the weight of the watermarking loss function. The proposed algorithm is tested on three datasets and three mainstream deep learning models. DWA-Watermarking embedding mechanism accelerates the speed of watermarking embedding and improves the extraction rate of the watermarking in the model
Rotating target detection algorithm in remote sensing images based on improved YOLOv5s

Aiming at the problem of low target detection accuracy and easily missed detection in remote sensing images, a rotating target detection method based on YOLOv5s is proposed with YOLOv5s as the basic framework. First of all, the circular smooth label CSL (Circular Smooth Label, CSL) is used to realize the conversion of angle regression prediction to classification prediction to overcome the problems of regression prediction angle periodicity and variable boundary and improve the detection accuracy. Secondly, the densely coded label (Densely Coded Label, DCL) replaces the sparsely coded label, dramatically reducing the thickness of the prediction layer and improving the training speed. Finally, in the C3 module of the original YOLOv5s feature extraction and feature fusion network, a lightweight channel attention mechanism is introduced into the module, thereby improving the network's local feature capture and fusion capabilities. The experimental results show that on the public DOTA remote sensing image dataset, the map results of the improved YOLOv5s_obb reach 0.78, respectively. Compared with the original YOLOv5s, this method can be improved by 1.7%, and compared with other typical remote sensing target detection methods, the accuracy has also been improved, which proves the effectiveness of the improved YOLOv5s_obb method.
Research on Mask Wearing Detection Algorithm Based on Pedestrian Detection in the Post-Pandemic

The mask wearing detection algorithm based on computer vision in this article can automatically detect whether a person is wearing a mask, which can greatly improve efficiency compared to manual detection. These algorithms are mainly divided into two categories: one is in active scenes, usually collecting a single facial image and using image classification algorithms to determine whether to wear a mask; Another type is in passive scenarios, where object detection algorithms are typically used to locate and classify all faces in the image to determine whether each face is wearing a mask. This article attempts another detection method in passive scenarios, which is based on pedestrian detection for mask wearing detection. This method combines pedestrian detection with mask detection, with the advantage that wearing a mask can be used as an attribute of the pedestrian detection frame, which can be easily combined with technology such as target tracking and applied to a wider range of tasks.
Study on Characteristics and Voltage Regulation of Emergency Diesel Generator in Nuclear Power Plant

In this paper, the external characteristics of the generator are studied, and the voltage characteristics of an emergency diesel generator are analyzed. According to the operation characteristics of an emergency diesel generator in a nuclear power plant, the relationship between the transfer function parameters of the generator excitation system and the regulation characteristics is analyzed, which shows and verifies that the emergency diesel generator is in operation.
Remote Sensing Image Captioning Using Hire-MLP

Remote sensing image captioning has been widely applied to traffic management, geographic research, etc. Although the neural network approach has been successfully improving the performance of the Remote sensing image captioning system, Remote sensing image captioning is still facing object identification challenges due to the small size of objects, uneven distribution, and high coupling with the surrounding image background. In this paper, we propose a novel remote sensing image captioning encoder-decoder model Hierarchical rearrangement-Multi-Layer Perceptron (HMLP) whose encoder adapts hierarchical rearrangement-multi-layer perceptron to improve the capability of objection recognition. Extensive experiments have been conducted to testify HMLP by three datasets RSCID, UCM-caption, and NWPU-caption. Results show that HMLP outperforms many image captioning systems in the evaluation metrics BLEU4, METEOR, ROUGE-L, and CIDEr.
Light Field Salient Object Detection Based on Spatial Feature Compensation

The Light Field uses a new imaging technology that records both the intensity and direction of light in space. Compared to RGB and RGB-D images, light field data contains information about the color, intensity, position and direction of light, which is closer to the human perception of natural scenes. We propose a light field salient object detection algorithm based on spatial feature compensation(SFCNet). Firstly, this algorithm uses discrete gray-scale multi-view images as input to compress the amount of data. Based on this, we design a spatial feature aggregation module to integrate multi-view features and RGB features. In addition, a convolutional noise reduction module is designed to reduce the effect of noise in the depth information to address the problem of poor depth information. Finally, we discuss the way of multi-feature fusion for light field data. Experiments demonstrate that our algorithm achieves high accuracy detection results while effectively reducing the data size.
Fault Line Selection of Low Current Grounding Based on LSTM Neural Network

This paper introduces the basic principles of LSTM neural networks and applies LSTM neural networks to low-current ground fault selection. The small current ground fault model is constructed by Simulink simulation software, and the relevant data of the fundamental feature component, the fifth harmonic feature component and the wavelet energy feature component are obtained, and the LSTM neural network is trained, which verifies the accuracy and feasibility of the method.
MUNformer: A strong encoder that uses multi-level features extracted by different feature extractors for fusion

Medical image segmentation technology is an impo rtant foundation for the development of modern medical technolo gy and has significant implications for the diagnosis and treatmen t of diseases. For many years, convolutional neural networks (CN N) have dominated computer vision, especially innovative neural networks with higher architectures, such as U-shaped models and pyramid pooling techniques, which can be applied to various me dical image segmentation tasks. However, due to the limitation of receptive field in convolution, CNN can not capture long distance context information and lacks spatial interactivity, which is beyo nd doubt. On the other hand, although Transformer has the glob al Receptive field capture feature, it does not capture local featur es as well as CNN. In this article, we propose MUNformer, a mult i-scale global semantic feature transformer-based extractor and Unet-based architecture, a type of Transformer that focuses on m ulti-scale network structures for semantic segmentation. MUNfor mer has two attractive features: 1) MUNformer extracts features at different levels from two feature extractors in the encoder part to enrich Semantic information. 2) The decoder interacts with th e features extracted by two different feature extractors in the enc oder (features at the corresponding level), combines local attentio n and global attention to present a powerful representation, and f inally performs feature stitching to obtain more abundant Seman tic information. We believe that the combination of CNN and Tra nsformer can extract powerful feature representations through m ulti-level interaction, utilizing the advantages of both. MUNforme r performs better than various competitive methods in different medical applications, including multi organ segmentation and he art segmentation.
Research on Blockchain-based Information System for Power business archives Co-construction

With the continuous popularization of the emerging technology of “big cloud mobile intelligence chain”, the environment, object and content of power equipment operation and maintenance status and archive management had undergone tremendous changes. This paper discusses the existing technical solutions and shortcomings of the power business archives information system, introduces the blockchain technology, and designs the power business archives information system model on the Hyperledger Fabric alliance chain technology platform based on the natural compatibility between the power business archives information system and the blockchain technology, and constructs the integration architecture of the business system and the archives system. Build a closed loop management system for the whole process of business-archival, and realize the power business archives information system of joint construction, data management and information sharing. It improves the reliability of electronic archives and provides convenience for the management and use of Power business archives
Fast and high-quality video inpainting by efficiently patch searching and synthesizing

The rapid development of computer technology leads to an explosive growth in video resources in the internet. Efficient video transmission is appealing for quickly deliver and facilely video quality preservation. Video inpainting is the key solution to the quality problem when data loss occurs during the transmission. The technology aims to restore the missing areas with appropriate data from the rest of the video, which is still challenging due to the high requirement on the inpainted result that can keep visual consistency with existing video content. In this paper, we propose a novel video inpainting method, which relies on the optical flow calculation and local pixel-based matching to achieve quick recovery of the missing areas in videos. Especially, we calculate the optical flow characteristics of video and use them to fast locate and search out the best-match patch. The qualitative and quantitative evaluations in DAVIS and YouTube-VOS datasets demonstrate that the proposed method yields competitive inpainted results and outperforms other pixel-based approaches.
Object Detection in UAV Aerial Images Based on Improved YOLOv7-tiny

With the wide application of unmanned aerial vehicle (UAV), the problem of object detection in UAV aerial image has attracted more and more attention. In order to detect objects in UAV aerial images more accurately, we made a series of improvements on the basis of YOLOv7-tiny to make it more suitable for detecting objects in UA V aerial images. Specifically, we added Global Attention Mechanism (GAM) to YOLOv7-tiny's neck network to better extract the features of the objects. We also introduced the Bidirectional Feature Pyramid Network (BiFPN) into the Neck network to enhance feature fusion capability. In order to detect small objects more accurately, we added a small object detection head in the YOLOv7-tiny Head section. Contextual Transformer module was introduced to make the network pay more attention to context information. To make the predicted box match the ground truth box more closely, we applied the SIoU loss function to YOLOv7-tiny. The experimental results showed that, mAP@0.5 is 38.1 % and mAP@0.5:0.95 is 21.3% by improved YOLOv7-tiny algorithm on VisDrone dataset. Compared with YOLOv7-tiny, mAP@0.5 is improved by 2.8% and mAP@0.5:0.95 is improved by 3%, which improved the accuracy of object detection in UAV aerial images.
Insulator Image Dataset Generation based on Generative Adversarial Network

For the intelligent processing of power equipment, its image datasets are often required as data support. However, the collection of power equipment image datasets is limited by the location and environment, and the number of collected datasets is relatively small, which cannot provide enough data for specific applications. This paper proposes to use image generation function of generative adversarial network to generate more images of electrical equipment from existing datasets with a smaller number, thereby increasing the size of electrical equipment datasets. In this paper, the insulator dataset is mainly used for experiment. First, the model building of the generative adversarial network is carried out. This paper uses pytorch to build three network model frameworks of generative adversarial networks, namely self-attention generative adversarial network, boundary equilibrium generative adversarial network and projected generative adversarial network, and selects relevant loss function and training method according to characteristics of each model. Second, models are trained on these three GAN s for the insulator image dataset and analyze experimental results of these three models. Finally, generated results and FID scores of the three models are compared. The FID score of projected generative adversarial network is the lowest, and the quality and diversity of the generated samples are the best, indicating that this model can better learn the characteristics of images and is more suitable for the generation of power equipment datasets.
A Research on CSR-DCF Tracking Algorithm based on YOLO Detection

A new algorithm for target tracking has been proposed, which combines the YOLO object detection algorithm with the CSR-DCF tracking algorithm. It includes an added self-attention mechanism to address the problem of losing targets during tracking in the DCF algorithm. For practical tracking, a closed-loop control strategy is adopted, using the YOLO algorithm to detect targets and form a tracking closed loop to re-track lost targets. This algorithm demonstrates excellent performance compared to traditional CSR-DCF and DCF target tracking algorithms, with good tracking results, real-time performance, and the ability to re-track lost targets.
Structure-preserving Domain Adaptation Network for Generating Pencil Sketches

The paper presents a structure-preserving domain adaptation network, termed SDANet. To address the problem of edge blurring or incompleteness in the pencil sketches generated by existing methods, we designed a structure-preserving module to extract the edges of both the real-world photos and the generated real-world photos, then constrained them with a structure-preserving loss, in order to produce clearer edges in the generated pencil sketches. In addition, we constructed a real-world photo and pencil sketch dataset for training the pencil sketches generation network, named RW-PS, which comprises an extensive collection of high-quality pencil sketches. Experimental results demonstrate that our method generates pencil sketches with a lowest FID score compared to existing SOTA methods on the RW-PS dataset. Moreover, in user surveys, the pencil sketches generated by our proposed method received the highest number of votes.
APENet: Adaptive Pyramid Pooling and SE Network for Superpixel Segmentation

Superpixel segmentation is a commonly used computer vision image segmentation method that aims to cluster similar pixels in an image into larger, more meaningful regions. Traditional superpixel segmentation algorithms are non-differentiable and usually hard to extract multi-scale features. To solve this problem, this paper proposes Adaptive Pyramid Pooling Module (APPM) and SE attention module (SEM) for the superpixel segmentation algorithm. APPM can introduce the multi-scale feature, And SEM can add the channel interaction into the superpixel feature. Experimental results show that the algorithm can achieve state-of-the-art performance in the BSDS500 dataset.
Energy Consumption Optimization of Swin Transformer Based on Local Aggregation and Group-Wise Transformation

The Transformer is a natural language processing model that uses attention mechanisms, originally introduced by Google in 2017. Due to its excellent feature extraction capabilities, researchers in the industry have explored the application of Transformer in computer vision tasks. Following the success of the Vision Transformer in 2020, the Swin Transformer was introduced, which outperforms traditional convolutional neural networks on various benchmarks in the vision field. As a result, Swin Transformer has become a popular backbone for practical production applications. While Swin Transformer excels in accuracy, its large parameter size and high energy consumption pose challenges for deployment at the edge. Addressing this issue is crucial for the wide-scale adoption of Swin Transformer. Current research on energy consumption for Transformer-based models is still in its early stages. To reduce model size and energy consumption while preserving accuracy, this study focuses on model compression. Specifically, we propose a combination of group-wise transformation and local aggregation methods from traditional CNN models. By introducing local aggregation and group-wise transformation calculation methods while maintaining the original shifted window attention mechanism of Swin Transformer, the proposed approach effectively reduces the number of model parameters. Our experimental results show that the Swin-Transformer model trained with this method on an RTX-3090 graphics card can save up to 24% of energy, with an accuracy improvement of about 1%.